Layer four optimization for a virtual network defined over public cloud

Description

BACKGROUND

Today, a corporate enterprise network is the communication backbone that securely connects the different offices and divisions of a corporation. This network is typically a wide area network (WAN) that connects (1) users in branch offices and regional campuses, (2) corporate datacenters that host business applications, Intranets and their corresponding data, and (3) the global Internet through corporate firewalls and DMZ (demilitarized zone). Enterprise networks include specialized hardware such as switches, routers and middlebox appliances interconnected by expensive leased lines, such as Frame Relay and MPLS (multiprotocol label switching).

In the last several years, there has been a paradigm shift in the way corporations serve and consume communication services. First, the mobility revolution has allowed users to access services from any place at any time using mobile devices, mostly smart phones. Such users access the business services through public Internet and cellular networks. At the same time, third-party SaaS (Software as a Service) vendors (e.g., Salesforce, Workday, Zendesk) have replaced traditional on-premise applications, while other applications hosted in private datacenters have been relocated to the public clouds. While this traffic is still carried within the enterprise network, a significant portion of it originates and terminates outside the corporate network perimeters and has to cross both the public Internet (once or twice) as well as the corporate network. Recent studies have shown that 40% of corporate networks report that the percentage of backhauled traffic (i.e., of Internet traffic observed in the corporate network) is above 80%. This means that the majority of the corporate traffic is carried over both expensive leased lines and the consumer Internet.

As a consumer-centric service, the Internet itself is a poor medium for business traffic. It lacks the reliability, QoS (quality of service) guarantees and security expected by critical business applications. Moreover, the ever-increasing consumer traffic demands, net-neutrality regulations and the creation of Internet bypasses by major players (e.g., Netflix, Google, public clouds) have lowered the monetary return per traffic unit. These trends have reduced the incentives of service providers to quickly catch up with the consumer demands and offer adequate business services.

Given the growth of public clouds, corporations are migrating more of their compute infrastructure to the public cloud datacenters. Public cloud providers have been at the forefront of compute and networking infrastructure investment. These cloud services have built many datacenters across the world, with Azure, AWS, IBM and Google expanding to 38, 16, 25, and 14 worldwide regions respectively in 2016. Each public cloud provider has interconnected its own datacenters by using expensive high-speed networks that employ dark fiber and undersea cables deployed by submarines.

Today, notwithstanding these changes, corporate network policies often force all corporate traffic to go through their secure WAN gateways. As users become mobile and applications migrate to SaaS and public clouds, corporate WANs become costly detours that slow down all corporate communications. Most corporate WAN's traffic is either sourced from or destined to the Internet. Alternate secure solutions that send this traffic through the Internet are not adequate because of their poor and unreliable performance.

In the past few years, SD-WAN (Software-Defined WAN) vendors have addressed the expensive and limited-bandwidth branch office's last-mile connectivity (typically T1 or T3) to the WAN backbone and the Internet. These vendors leverage a bundle of consumer-grade broadband technologies (e.g., DOCSIS, ADSL, LTE) as well as MPLS to carry much more traffic with an extended reliability compared with using a single broadband Internet access. Their technology is mostly based on optimizing and prioritizing application traffic across the bundle, typically performed between appliances placed in the remote office and datacenter. Since they rely on appliances, they often do not address mobile devices or IoT. More significantly, they also often do not address the mid-mile (i.e., long-haul) connectivity issues in the corporate WAN, and often connect to MPLS mid-mile backbones provided by telcos, which can also provide their SD-WAN solution.

Another second category of SD-WAN vendors address the mid-mile issues, by maintaining a private worldwide network based on hosted points of presence (typically at the datacenters of the telcos) and leased lines, often based on MPLS. However, by relying on the old model of leased lines, these vendors suffer from the same cost and limited-capacity issues. Microsoft Azure has recently announced a mid-mile WAN service termed Azure Virtual WAN. This cloudified Virtual WAN is using the Azure cloud network as a long-haul part of corporate WAN. It relies on a hub-and-spoke configuration to connect multiple branches, using a single shared hub located in the Azure cloud.

BRIEF SUMMARY

Some embodiments establish for an entity a virtual network over several public cloud datacenters of one or more public cloud providers in one or more regions (e.g., several cities, states, countries, etc.). An example of an entity for which such a virtual network can be established include a business entity (e.g., a corporation), a non-profit entity (e.g., a hospital, a research organization, etc.), and an educational entity (e.g., a university, a college, etc.), or any other type of entity. Examples of public cloud providers include Amazon Web Services (AWS), Google Cloud Platform (GCP), Microsoft Azure, etc.

In some embodiments, high-speed, reliable private networks interconnect two or more of the public cloud datacenters (the public clouds). Some embodiments define the virtual network as an overlay network that spans across several public clouds to interconnect one or more private networks (e.g., networks within branches, divisions, departments of the entity or their associated datacenters), mobile users, SaaS (Software as a Service) provider machines, machines and/or services in the public cloud(s), and other web applications.

The virtual network in some embodiments can be configured to optimize the routing of the entity's data messages to their destinations for best end-to-end performance, reliability and security, while trying to minimize the routing of this traffic through the Internet. Also, the virtual network in some embodiments can be configured to optimize the layer 4 processing of the data message flows passing through the network. For instance, in some embodiments, the virtual network optimizes the end-to-end rate of TCP (Transport Control Protocol) connections by splitting the rate control mechanisms across the connection path.

Some embodiments establish the virtual network by configuring several components that are deployed in several public clouds. These components include in some embodiments software-based measurement agents, software forwarding elements (e.g., software routers, switches, gateways, etc.), layer-4 connection proxies and middlebox service machines (e.g., appliances, VMs, containers, etc.). One or more of these components in some embodiments use standardized or commonly available solutions, such as Open vSwitch, OpenVPN, strongSwan, and Ryu.

Some embodiments establish different virtual networks for different entities over the same set of public clouds of the same public cloud providers and/or over different sets of public clouds of the same or different public cloud providers. In some embodiments, a virtual network provider provides software and services that allow different tenants to define different virtual networks over the same or different public clouds. In some embodiments, the same controller cluster or different controller clusters can be used to configure the public cloud components to implement different virtual networks over the same or different sets of public clouds for several different entities.

To deploy a virtual network for a tenant over one or more public clouds, the controller cluster (1) identifies possible ingress and egress routers for entering and exiting the virtual network for the tenant based on locations of the tenant's branch offices, datacenters, mobile users, and SaaS providers, and (2) identifies routes that traverse from the identified ingress routers to the identified egress routers through other intermediate public-cloud routers that implement the virtual network. After identifying these routes, the controller cluster propagates these routes to the forwarding tables of the virtual network routers in the public cloud(s). In the embodiments that use OVS-based virtual network routers, the controller distributes the routes by using OpenFlow.

Some embodiments of the invention use layer 4 session splitting (e.g., TCP splitting) in order to optimize the traversal of data message flows across the virtual network. Under this approach, when traffic from a source to a destination passes through two public clouds of one or more public cloud providers, it passes through two public cloud relays (e.g., two virtual machines operating as relays) in the two public clouds, and one or both of these relays performs layer 4 session splitting. In the discussion below, the layer 4 splitting that is described is TCP splitting, although one of ordinary skill will realize that in other embodiments other layer 4 splitting operations are performed.

The TCP splitting of some embodiments provides significantly better download times for large flows. To improve performance for smaller flows, some embodiments use optimized TCP split implementations, such as using predefined pools of threads to handle SYN requests and predefined pools of connections to forward SYN requests in the cloud relays. Some embodiments also use an aggressive intra-cloud congestion control processes to gain additional improvements. Some embodiments also use redundant cloud relays and optimize across these redundant relays to obtain multiple paths through the public clouds.

Because the virtual network architecture of some embodiments is based on the public clouds, the deployed virtual networks benefit from the management of the public clouds, have low loss rates, are easily accessible to IT administrators, and can be managed by the VMs deployed in the public clouds by the virtual network provider (VNP). This architecture also does not rely on installing special software or using a special appliance at the tenant site or tenant machines, i.e., branch, mobile user or IoT. As mentioned above, the virtual networks in some embodiments is deployed by using standard edge VPN software, e.g., based on IPSec, that securely connect each user to a cloud relay.

Also, by deploying VMs in different public clouds to implement the MFN components, multiple virtual networks can be defined for multiple different entities over multiple public clouds. These virtual networks have the typical benefits of the public clouds, from the low costs, pay-as-you-go payment model, to the high data rate and throughput provided by the clouds (e.g., 10 cents per GB, with 2 Gbps throughput per VM, as compared to a connection of a remote branch over MPLS, which is typically 1-3 Mbps or less with a cost that is an order of magnitude more for a typical branch).

This approach is also not tied to the infrastructure of any one public cloud provider. Solutions that are tied to a single cloud provider often are based on a single fixed shared cloud relay. Such an approach only works for corporate branches in a single geographical region but is not scalable to a global corporate WAN. For instance, it would not make sense that two branches located in New York communicate through the unique corporate virtual hub located in San Francisco—not to mention global branches.

The virtual network approach of the embodiments described above follows the path of computing and storage in their transition from resource-limited on-premises computing to resource-abundant cloud computing. Like storage and computing, the virtual network approach of some embodiments offers a virtually unlimited resource, i.e., network capacity, and virtualization enables delegating local tasks to the cloud, such as interdomain routing and congestion control.

The preceding Summary is intended to serve as a brief introduction to some embodiments of the invention. It is not meant to be an introduction or overview of all inventive subject matter disclosed in this document. The Detailed Description that follows and the Drawings that are referred to in the Detailed Description will further describe the embodiments described in the Summary as well as other embodiments. Accordingly, to understand all the embodiments described by this document, a full review of the Summary, Detailed Description, the Drawings and the Claims is needed. Moreover, the claimed subject matters are not to be limited by the illustrative details in the Summary, Detailed Description and the Drawing.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features of the invention are set forth in the appended claims. However, for purposes of explanation, several embodiments of the invention are set forth in the following figures.

FIG. 1A presents a virtual network that is defined for a corporation over several public cloud datacenters of two public cloud providers.

FIG. 1B illustrates an example of two virtual networks for two corporate tenants that are deployed over the public clouds.

FIG. 1C alternatively illustrates an example of two virtual networks, with one network deployed over public clouds and the other virtual network deployed over another pair of public clouds.

FIG. 2 illustrates an example of a managed forwarding node and a controller cluster of some embodiments of the invention.

FIG. 3 illustrates an example of a measurement graph that the controller measurement-processing layer produces in some embodiments.

FIG. 4A illustrates an example of a routing graph that the controller path-identifying layer produces in some embodiments from the measurement graph.

FIG. 4B illustrates an example of adding known IPs for two SaaS providers to the two nodes in the routing graph that are in datacenters that are closest to the datacenters of these SaaS providers.

FIG. 4C illustrates a routing graph that is generated by adding two nodes to represent two SaaS providers.

FIG. 4D illustrates a routing graph with additional nodes added to represent branch offices and datacenters with known IP addresses that connect respectively to two public clouds.

FIG. 5 illustrates a process that the controller path-identifying layer uses to generate a routing graph from a measurement graph received from the controller measurement layer.

FIG. 6 illustrates the IPsec data message format of some embodiments.

FIG. 7 illustrates an example of the two encapsulating headers of some embodiments, while FIG. 8 presents an example that illustrates how these two headers are used in some embodiments.

FIGS. 9-11 illustrate message-handling processes that are performed respectively by the ingress, intermediate, and egress MFNs when they receive a message that is sent between two compute devices in two different branch offices.

FIG. 12 illustrates an example that does not involve an intermediate MFN between the ingress and egress MFNs.

FIG. 13 illustrates a message-handling process that is performed by the CFE of the ingress MFN when it receives a message that is sent from a corporate compute device in a branch office to another device in another branch office or in a SaaS provider datacenter.

FIG. 14 illustrates the NAT operation being performed at the egress router.

FIG. 15 illustrate a message-handling process that is performed by the ingress router that receives a message that is sent from a SaaS provider machine to a tenant machine.

FIG. 16 illustrates such TM engines that are placed in each virtual-network gateway that is on the virtual network's egress path to the Internet.

FIG. 17 illustrates a double-NAT approach that is used in some embodiments instead of the single NAT approach illustrated in FIG. 16.

FIG. 18 presents an example that illustrates the source port translation of the ingress NAT engine.

FIG. 19 illustrates the processing of a reply message that a SaaS machine sends in response to its processing of a data message of FIG. 18.

FIG. 20 presents an example that shows M virtual corporate WANs for M tenants of a virtual network provider that has network infrastructure and controller cluster(s) in N public clouds of one or more public cloud providers.

FIG. 21 conceptually illustrates a process performed by the controller cluster of the virtual network provider to deploy and manage a virtual WAN for a particular tenant.

FIG. 22 illustrates a three-layer SaaS deployment model of some embodiments.

FIG. 23 illustrates a two-layer SaaS deployment model of some embodiments.

FIG. 24 illustrates a process used by the central controller cluster of some embodiments to define routes for a multi-homed, multi-machine compute node (MMCN).

FIG. 25 presents an example of two branch nodes of two MMCNs and a SaaS datacenter.

FIG. 26 illustrates a process used by the central controller cluster of some embodiments to define routes for multi-homed SaaS providers.

FIG. 27 illustrates an example of an optimized virtual network that relies on two cloud relays that perform TCP split optimization.

FIG. 28 illustrates a default data transport mechanism for the example of FIG. 27.

FIG. 29 illustrates an ideal clean-slate approach for requesting content, where the request for the content would go through directly, triggering the transmission of all response packets.

FIG. 30 illustrates a case where client and server machines establish an end-to-end connection by performing a three-way TCP handshake.

FIG. 31 illustrates a TCP-split approach of some embodiments of the invention. Under this approach, the client- and server-side cloud relays act as TCP connection endpoints by performing TCP split operations.

FIG. 32 illustrates an example that shows that when the client- and server-side cloud relays perform TCP split operations, the TTFB and overall download time are adversely affected for small flows.

FIG. 33 illustrates the use of an early-SYN approach to remove the SYN-ACK and ACK delays.

FIG. 34 illustrates the use of a thread pool to remove fork delays.

FIG. 35 illustrates the use of a connection pool to eliminate the connection setup delay.

FIG. 36 illustrates the use of a Turbo-Start TCP eliminating the TCP-window incrementing delay.

FIG. 37 illustrates K-split module of a cloud relay that is implemented on a host computer in a public cloud datacenter in some embodiments.

FIG. 38 illustrates a process that the K-split module of some embodiments performs once it receives a Netfilter interrupt that this filter has captured a first SYN packet.

FIGS. 39-44 illustrates several allocation and pairing examples to explain the operation of the process of FIG. 38.

FIG. 45 conceptually illustrates a computer system with which some embodiments of the invention are implemented.

DETAILED DESCRIPTION

In the following detailed description of the invention, numerous details, examples, and embodiments of the invention are set forth and described. However, it will be clear and apparent to one skilled in the art that the invention is not limited to the embodiments set forth and that the invention may be practiced without some of the specific details and examples discussed.

Some embodiments define the virtual network as an overlay network that spans across several public cloud datacenters (public clouds) to interconnect one or more private networks (e.g., networks within branches, divisions, departments of the entity or their associated datacenters), mobile users, SaaS (Software as a Service) provider machines, machines and/or services in the public cloud(s), and other web applications. In some embodiments, high-speed, reliable private networks interconnect two or more of the public cloud datacenters.

Some embodiments utilize a logically centralized controller cluster (e.g., a set of one or more controller servers) that configures the public-cloud components to implement the virtual network over several public clouds. In some embodiments, the controllers in this cluster are at various different locations (e.g., are in different public cloud datacenters) in order to improve redundancy and high availability. When different controllers in the controller cluster are located in different public cloud datacenters, the controllers in some embodiments share their state (e.g., the configuration data that they generate to identify tenants, routes through the virtual networks, etc.). The controller cluster in some embodiments scales up or down the number of public cloud components that are used to establish the virtual network, or the compute or network resources allocated to these components.

Several examples of corporate virtual networks are provided in the discussion below. However, one of ordinary skill will realize that some embodiments define virtual networks for other types of entities, such as other business entities, non-profit organizations, educational entities, etc. Also, as used in this document, data messages refer to a collection of bits in a particular format sent across a network. One of ordinary skill in the art will recognize that the term data message is used in this document to refer to various formatted collections of bits that are sent across a network. The formatting of these bits can be specified by standardized protocols or non-standardized protocols. Examples of data messages following standardized protocols include Ethernet frames, IP packets, TCP segments, UDP datagrams, etc. Also, as used in this document, references to L2, L3, L4, and L7 layers (or layer 2, layer 3, layer 4, and layer 7) are references respectively to the second data link layer, the third network layer, the fourth transport layer, and the seventh application layer of the OSI (Open System Interconnection) layer model.

FIG. 1A presents a virtual network 100 that is defined for a corporation over several public cloud datacenters 105 and 110 of two public cloud providers A and B. As shown, the virtual network 100 is a secure overlay network that is established by deploying different managed forwarding nodes 150 in different public clouds and connecting the managed forwarding nodes (MFNs) to each other through overlay tunnels 152. In some embodiments, an MFN is a conceptual grouping of several different components in a public cloud datacenter that with other MFNs (with other groups of components) in other public cloud datacenters establish one or more overlay virtual networks for one or more entities.

As further described below, the group of components that form an MFN include in some embodiments (1) one or more VPN gateways for establishing VPN connections with an entity's compute nodes (e.g., offices, private datacenters, remote users, etc.) that are external machine locations outside of the public cloud datacenters, (2) one or more forwarding elements for forwarding encapsulated data messages between each other in order to define an overlay virtual network over the shared public cloud network fabric, (3) one or more service machines for performing middlebox service operations as well as L4-L7 optimizations, and (4) one or more measurement agents for obtaining measurements regarding the network connection quality between the public cloud datacenters in order to identify desired paths through the public cloud datacenters. In some embodiments, different MFNs can have different arrangements and different numbers of such components, and one MFN can have different numbers of such components for redundancy and scalability reasons.

Also, in some embodiments, each MFN's group of components execute on different computers in the MFN's public cloud datacenter. In some embodiments, several or all of an MFN's components can execute on one computer of a public cloud datacenter. The components of an MFN in some embodiments execute on host computers that also execute other machines of other tenants. These other machines can be other machines of other MFNs of other tenants, or they can be unrelated machines of other tenants (e.g., compute VMs or containers).

The virtual network 100 in some embodiments is deployed by a virtual network provider (VNP) that deploys different virtual networks over the same or different public cloud datacenters for different entities (e.g., different corporate customers/tenants of the virtual network provider). The virtual network provider in some embodiments is the entity that deploys the MFNs and provides the controller cluster for configuring and managing these MFNs.

The virtual network 100 connects the corporate compute endpoints (such as datacenters, branch offices and mobile users) to each other and to external services (e.g., public web services, or SaaS services such as Office365 or Salesforce) that reside in the public cloud or reside in private datacenter accessible through the Internet. As further described below, SaaS in some embodiments is a software distribution model in which a third-party provider hosts applications and makes them available to customers over the Internet.

The virtual network 100 leverages the different locations of the different public clouds to connect different corporate compute endpoints (e.g., different private networks and/or different mobile users of the corporation) to the public clouds in their vicinity. Corporate compute endpoints are also referred to as corporate compute nodes in the discussion below. In some embodiments, the virtual network 100 also leverages the high-speed networks that interconnect these public clouds to forward data messages through the public clouds to their destinations or to get as close to their destinations while reducing their traversal through the Internet. When the corporate compute endpoints are outside of public cloud datacenters over which the virtual network spans, these endpoints are referred to as external machine locations. This is the case for corporate branch offices, private datacenters and devices of remote users.

In the example illustrated in FIG. 1A, the virtual network 100 spans six datacenters 105a-105f of the public cloud provider A and four datacenters 110a-110d of the public cloud provider B. In spanning these public clouds, this virtual network connects several branch offices, corporate datacenters, SaaS providers and mobile users of the corporate tenant that are located in different geographic regions. Specifically, the virtual network 100 connects two branch offices 130a and 130b in two different cities (e.g., San Francisco, California, and Pune, India), a corporate datacenter 134 in another city (e.g., Seattle, Washington), two SaaS provider datacenters 136a and 136b in another two cities (Redmond, Washington, and Paris, France), and mobile users 140 at various locations in the world. As such, this virtual network can be viewed as a virtual corporate WAN.

In some embodiments, the branch offices 130a and 130b have their own private networks (e.g., local area networks) that connect computers at the branch locations and branch private datacenters that are outside of public clouds. Similarly, the corporate datacenter 134 in some embodiments has its own private network and resides outside of any public cloud datacenter. In other embodiments, however, the corporate datacenter 134 or the datacenter of the branch 130a and 130b can be within a public cloud, but the virtual network does not span this public cloud, as the corporate or branch datacenter connects to the edge of the virtual network 100.

As mentioned above, the virtual network 100 is established by connecting different deployed managed forwarding nodes 150 in different public clouds through overlay tunnels 152. Each managed forwarding node 150 includes several configurable components. As further described above and further described below, the MFN components include in some embodiments software-based measurement agents, software forwarding elements (e.g., software routers, switches, gateways, etc.), layer 4 proxies (e.g., TCP proxies) and middlebox service machines (e.g., VMs, containers, etc.). One or more of these components in some embodiments use standardized or commonly available solutions, such as Open vSwitch, OpenVPN, strongSwan, etc.

In some embodiments, each MFN (i.e., the group of components the conceptually forms an MFN) can be shared by different tenants of the virtual network provider that deploys and configures the MFNs in the public cloud datacenters. Conjunctively, or alternatively, the virtual network provider in some embodiments can deploy a unique set of MFNs in one or more public cloud datacenters for a particular tenant. For instance, a particular tenant might not wish to share MFN resources with another tenant for security reasons or quality of service reasons. For such a tenant, the virtual network provider can deploy its own set of MFNs across several public cloud datacenters.

In some embodiments, a logically centralized controller cluster 160 (e.g., a set of one or more controller servers) operate inside or outside of one or more of the public clouds 105 and 110, and configure the public-cloud components of the managed forwarding nodes 150 to implement the virtual network over the public clouds 105 and 110. In some embodiments, the controllers in this cluster are at various different locations (e.g., are in different public cloud datacenters) in order to improve redundancy and high availability. The controller cluster in some embodiments scales up or down the number of public cloud components that are used to establish the virtual network, or the compute or network resources allocated to these components.

In some embodiments, the controller cluster 160, or another controller cluster of the virtual network provider, establishes a different virtual network for another corporate tenant over the same public clouds 105 and 110, and/or over different public clouds of different public cloud providers. In addition to the controller cluster(s), the virtual network provider in other embodiments deploys forwarding elements and service machines in the public clouds that allow different tenants to deploy different virtual networks over the same or different public clouds. FIG. 1B illustrates an example of two virtual networks 100 and 180 for two corporate tenants that are deployed over the public clouds 105 and 110. FIG. 1C alternatively illustrates an example of two virtual networks 100 and 182, with one network 100 deployed over public clouds 105 and 110 and the other virtual network 182 deployed over another pair of public clouds 110 and 115.

Through the configured components of the MFNs, the virtual network 100 of FIG. 1A allows different private networks and/or different mobile users of the corporate tenant to connect to different public clouds that are in optimal locations (e.g., as measured in terms of physical distance, in terms of connection speed, loss, delay and/or cost, and/or in terms of network connection reliability, etc.) with respect to these private networks and/or mobile users. These components also allow the virtual network 100 in some embodiments to use the high-speed networks that interconnect the public clouds to forward data messages through the public clouds to their destinations while reducing their traversal through the Internet.

In some embodiments, the MFN components are also configured to run novel processes at the network, transport and application layers to optimize the end-to-end performance, reliability and security. In some embodiments, one or more of these processes implement proprietary high-performance networking protocols, free from the current network protocol ossification. As such, the virtual network 100 in some embodiments is not confined by Internet autonomous systems, routing protocols, or even end-to-end transport mechanisms.

For example, in some embodiments, the components of the MFNs 150 (1) create optimized, multi-path and adaptive centralized routing, (2) provide strong QoS (Quality of Service) guarantees, (3) optimize end-to-end TCP rates through intermediate TCP splitting and/or termination, and (4) relocate scalable application-level middlebox services (e.g., firewalls, intrusion detection systems (IDS), intrusion prevention system (IPS), WAN optimization, etc.) to the compute part of the cloud in a global network function virtualization (NFV). Accordingly, the virtual network can be optimized to fit customized and changing demands of the corporation without being bound to existing network protocol. Also, in some embodiments, the virtual network can be configured as a “pay as you go” infrastructure that can be dynamically and elastically scaled up and down both in performance capability and in geographical span according to the continuous requirement changes.

To implement the virtual network 100, at least one managed forwarding node 150 in each public cloud datacenter 105a-105f and 110a-110d spanned by the virtual network has to be configured by the set of controllers. FIG. 2 illustrates an example of a managed forwarding node 150 and a controller cluster 160 of some embodiments of the invention. In some embodiments, each managed forwarding node 150 is a machine (e.g., a VM or container) that executes on a host computer in a public cloud datacenter. In other embodiments, each managed forwarding node 150 is implemented by multiple machines (e.g., multiple VMs or containers) that execute on the same host computer in one public cloud datacenter. In still other embodiments, two or more components of one MFN can be implemented by two or more machines executing on two or more host computers in one or more public cloud datacenters.

As shown, the managed forwarding node 150 includes a measurement agent 205, firewall and NAT middlebox service engines 210 and 215, one or more optimization engines 220, edge gateways 225 and 230, and a cloud forwarding element 235 (e.g., a cloud router). In some embodiments, each of these components 205-235 can be implemented as a cluster of two or more components.

The controller cluster 160 in some embodiments can dynamically scale up or down each component cluster (1) to add or remove machines (e.g., VMs or containers) to implement each component's functionality and/or (2) to add or remove compute and/or network resources to the previously deployed machines that implement that cluster's components. As such, each deployed MFN 150 in a public cloud datacenter can be viewed as a cluster of MFNs, or it can be viewed as a node that includes multiple different component clusters that perform different operations of the MFN.

Also, in some embodiments, the controller cluster deploys different sets of MFNs in the public cloud datacenters for different tenants for which the controller cluster defines virtual networks over the public cloud datacenters. In this approach, the virtual networks of any two tenants do not share any MFN. However, in the embodiments described below, each MFN can be used to implement different virtual networks for different tenants. One of ordinary skill will realize that in other embodiments the controller cluster 160 can implement the virtual network of each tenant of a first set of tenants with its own dedicated set of deployed MFNs, while implementing the virtual network of each tenant of a second set of tenants with a shared set of deployed MFNs.

In some embodiments, the branch gateway 225 and remote device gateway 230 establish secure VPN connections respectively with one or more branch offices 130 and remote devices (e.g., mobile devices 140) that connect to the MFN 150, as shown in FIG. 2. One example of such VPN connections are IPsec connections, which will be further described below. However, one of ordinary skill will realize that in other embodiments, such gateways 225 and/or 230 establish different types of VPN connections.

An MFN 150 in some embodiments includes one or more middlebox engines that perform one or more middlebox service operations, such are firewall operations, NAT operations, IPS operations, IDS operations, load balancing operations, WAN optimization operations, etc. By incorporating these middlebox operations (e.g., firewall operations, WAN optimization operations, etc.) in the MFNs that are deployed in the public cloud, the virtual network 100 implements in the public cloud much of the functions that are traditionally performed by the corporate WAN infrastructure at a corporation's datacenter(s) and/or branch office(s).

Accordingly, for many of the middlebox services, the corporate compute nodes (e.g., remote devices, branch offices and datacenters) no longer have to access the corporate WAN infrastructure of the corporation in a private datacenter or branch office, as much of these services are now deployed in the public clouds. This approach speeds up the access of the corporate compute nodes (e.g., remote devices, branch offices and datacenters) to these services, and avoids costly congested-network bottlenecks at private datacenters that would otherwise be dedicated to offering such services.

This approach effectively distributes the WAN gateway functionality to various MFNs in the public cloud datacenters. For instance, in the virtual network 100 of some embodiments, most or all of the traditional corporate WAN gateway security functions (e.g., firewall operations, intrusion detection operations, intrusion prevention operations, etc.) are moved to the public cloud MFNs (e.g., ingress MFNs at which data from compute endpoints is received into the virtual network). This effectively allows the virtual network 100 to have a distributed WAN gateway that is implemented at many different MFNs that implement the virtual network 100.

In the example illustrated in FIG. 2, the MFN 150 is shown to include the firewall engine 210, the NAT engine 215 and one or more L4-L7 optimization engines. One of ordinary skill will realize that in other embodiments, the MFN 150 includes other middlebox engines for performing other middlebox operations. In some embodiments, the firewall engine 210 enforces firewall rules on (1) data message flows on their ingress paths into the virtual network (e.g., on data message flows that the gateways 225 and 230 receives and process from branch offices 130 and mobile devices 140) and (2) data messages flows on their egress paths out of the virtual network (e.g., on data message flows that are sent to SaaS provider datacenters through the NAT engine 215 and the Internet 202).

The firewall engine 210 of the MFN 150 in some embodiments also enforces firewall rules when the firewall engine belongs to an MFN that is an intermediate hop between an ingress MFN at which a data message flow enters a virtual network and an egress MFN at which the data message flow exits the virtual network. In other embodiments, the firewall engine 210 only enforces firewall rules when it is part of a data message flow's ingress MFN and/or egress MFN.

In some embodiments, the NAT engine 215 performs a network address translation to change the source network addresses of data message flows on their egress paths out of the virtual network to third party devices (e.g., to SaaS provider machines) through the Internet 202. Such network address translations ensure that third-party machines (e.g., SaaS machines) can be properly configured to process the data message flows that without the address translations might specify private network addresses of the tenants and/or the public cloud providers. This is particularly problematic as private network addresses of different tenants and/or cloud providers might overlap. The address translation also ensures that the reply messages from the third party devices (e.g., the SaaS machines) can be properly received by the virtual network (e.g., by the MFN NAT engine from which the message exited the virtual network).

The NAT engines 215 of the MFNs in some embodiments perform double-NAT operations on each data message flow that leaves the virtual network to reach a third party machine, or that enters the virtual network from a third party machine. As further described below, one NAT operation in the two NAT operations is performed on such a data message flow at its ingress MFN when it enters the virtual network, while the other NAT operation is performed on the data message flow at its egress MFN when it exits the virtual network.

This double NAT approach allows more tenant private networks to be mapped to the networks of the public cloud providers. This approach also reduces the load for distributing to the MFNs data regarding changes to tenant private networks. Before the ingress or egress NAT operations, some embodiments perform a tenant mapping operation that uses the tenant identifier to first map the tenant's source network address to another source network address that is then mapped to yet another source network address by the NAT operation. Performing the double NAT operation reduces the data distribution load for distributing data regarding changes to the tenant private networks.

The optimization engine 220 executes novel processes that optimize the forwarding of the entity's data messages to their destinations for best end-to-end performance and reliability. Some of these processes implement proprietary high-performance networking protocols, free from the current network protocol ossification. For example, in some embodiments, the optimization engine 220 optimizes end-to-end TCP rates through intermediate TCP splitting and/or termination.

The cloud forwarding element 235 is the MFN engine that is responsible for forwarding a data message flow to the next hop MFN's cloud forwarding element (CFE) when the data message flow has to traverse to another public cloud to reach its destination, or to an egress router in the same public cloud when the data message flow can reach its destination through the same public cloud. In some embodiments, the CFE 235 of the MFN 150 is a software router.

To forward the data messages, the CFE encapsulates the messages with tunnel headers. Different embodiments use different approaches to encapsulate the data messages with tunnel headers. Some embodiments described below use one tunnel header to identify network ingress/egress addresses for entering and exiting the virtual network, and use another tunnel header to identify next hop MFNs when a data message has to traverse one or more intermediate MFN to reach the egress MFN.

Specifically, in some embodiments, the CFE sends the data message with two tunnel headers (1) an inner header that identifies an ingress CFE and egress CFE for entering and exiting the virtual network, and (2) an outer header that identifies the next hop CFE. The inner tunnel header in some embodiments also includes a tenant identifier (TID) in order to allow multiple different tenants of the virtual network provider to use a common set of MFN CFEs of the virtual network provider. Other embodiments define tunnel headers differently in order to define the overlay virtual network.

To deploy a virtual network for a tenant over one or more public clouds, the controller cluster (1) identifies possible ingress and egress routers for entering and exiting the virtual network for the tenant based on locations of the tenant's corporate compute nodes (e.g., branch offices, datacenters, mobile users and SaaS providers), and (2) identifies routes that traverse from the identified ingress routers to the identified egress routers through other intermediate public-cloud routers that implement the virtual network. After identifying these routes, the controller cluster propagates these routes to the forwarding tables of the MFN CFEs 235 in the public cloud(s). In the embodiments that use OVS-based virtual network routers, the controller distributes the routes by using OpenFlow.

In some embodiments, the controller cluster 160 can also configure the components 205-235 of each MFN 150 that implements the virtual network to optimize several network processing layers in order to achieve best end-to-end performance, reliability and security. For example, in some embodiments, these components are configured (1) to optimize layer 3 traffic routing (e.g., shortest path, packet duplication), (2) to optimize layer 4 TCP congestion control (e.g., segmentation, rate control), (3) to implement security features (e.g., encryption, deep packet inspection, firewall), and (4) to implement application-layer compression features (e.g., de-duplication, caching). Within the virtual network, corporate traffic is secured, inspected and logged.

In some embodiments, one measurement agent is deployed for each MFN in a public cloud datacenter. In other embodiments, multiple MFNs in a public cloud datacenter or in a collection of datacenters (e.g., in a collection of nearby, associated datacenters, such as datacenters in one availability zone) share one measurement agent. To optimize the layers 3 and 4 processing, the measurement agent 205 associated with each managed forwarding node 150 repeatedly generates measurement values that quantify the quality of the network connection between its node and each of several other “neighboring” nodes.

Different embodiments define neighboring nodes differently. For a particular MFN in one public cloud datacenter of a particular public cloud provider, a neighboring node in some embodiments includes (1) any other MFN that operates in any public cloud datacenter of the particular public cloud provider, and (2) any other MFN that operates in another public cloud provider's datacenter that is within the same “region” as the particular MFN.

Different embodiments define the same region differently. For instance, some embodiments define a region in terms of a distance that specifies a bounding shape around the particular managed forwarding node. Other embodiments define regions in terms of cities, states, or regional areas, such as northern California, southern California, etc. The assumption of this approach is that different datacenters of the same public cloud provider are connected with very high-speed network connections, while the network connections between the datacenters of different public cloud providers are likely fast when the datacenters are within the same region but likely not as fast when the datacenters are in different regions. The connection between the datacenters of different public cloud providers might have to traverse long distances through the public Internet when the datacenters are in different regions.

The measurement agent 205 generates measurement values differently in different embodiments. In some embodiments, the measurement agent sends pinging messages (e.g., UDP echo messages) periodically (e.g., once every second, every N seconds, every minute, every M minutes, etc.) to each of the measurement agents of its neighboring managed forwarding nodes. Given the small size of the pinging messages, they do not result in large network connection charges. For instance, for 100 nodes with each node sending a ping to each other node every 10 seconds, about 10 Kb/s of ingress and egress measurement traffic is generated for each node, and this leads to network consumption charges of a few dollars (e.g., $5) per node per year, given the current public cloud prices.

Based on the speed of the reply messages that it receives, the measurement agent 205 computes and updates measurement metric values, such as network-connection throughput speed, delay, loss, and link reliability. By repeatedly doing these operations, the measurement agent 205 defines and updates a matrix of measurement results that expresses the quality of network connections to its neighboring nodes. As the agent 205 interacts with the measurement agents of its neighboring nodes, its measurement matrix only quantifies the quality of the connections to its local clique of nodes.

The measurement agents of the different managed forwarding nodes send their measurement matrices to the controller cluster 160, which then aggregates all different clique connection data to obtain an aggregate mesh view of the connections between different pairs of managed forwarding nodes. When the controller cluster 160 collects different measurements for a link between two pairs of forwarding nodes (e.g., measurements taken by one node at different times), the controller cluster produces a blended value from the different measurements (e.g., produces an average or a weighted average of the measurements). The aggregate mesh view in some embodiments is a full mesh view of all the network connections between each pair of managed forwarding nodes, while in other embodiments it is a more complete view than the one produced by the measurement agents of the individual managed forwarding nodes.

As shown in FIG. 2, the controller cluster 160 includes a cluster of one or more measurement-processing engines 280, one or more path-identifying engines 282, and one or more management interfaces 284. In order not to obscure the description with unnecessary detail, each of these clusters will be referred to below in terms of singular engine or interface layers, i.e., in terms of a measurement-processing layer 280, a path-identifying layer 282, and a management interface layer 284.

The measurement-processing layer 280 receives the measurement matrices from the measurement agents 205 of the managed forwarding nodes and processes these measurements matrices to produce the aggregate mesh matrix that expresses the connection quality between different pairs of managed forwarding nodes. The measurement-processing layer 280 provides the aggregate mesh matrix to the path-identifying layer 282. Based on the aggregate mesh matrix, the path-identifying layer 282 identifies different desired routing paths through the virtual network for connecting different corporate data endpoints (e.g., different branch offices, corporate datacenters, SaaS provider datacenters and/or remote devices). This layer 282 then provides these routing paths in route tables that are distributed to the cloud forwarding elements 235 of the managed forwarding nodes 150.

In some embodiments, the identified routing path for each pair of data message endpoints is a routing path that is deemed optimal based on a set of optimization criteria, e.g., it is the fastest routing path, the shortest routing path, or the path that least uses the Internet. In other embodiments, the path-identifying engine can identify and provide (in the routing table) multiple different routing paths between the same two endpoints. In these embodiments, the cloud forwarding elements 235 of the managed forwarding nodes 150 then select one of the paths based on QoS criteria or other runtime criteria that they are enforcing. Each CFE 235 in some embodiments does not receive the entire routing path from the CFE to the egress point of the virtual network, but rather receives the next hop for the path.

In some embodiments, the path-identifying layer 282 uses the measurement values in the aggregate mesh matrix as inputs to routing algorithms that it executes to construct a global routing graph. This global routing graph is an aggregated and optimized version of a measurement graph that the measurement-processing layer 280 produces in some embodiments. FIG. 3 illustrates an example of a measurement graph 300 that the controller measurement-processing layer 280 produces in some embodiments. This graph depicts network connections between various managed forwarding nodes 150 in AWS and GCP public clouds 310 and 320 (i.e., in the datacenters of AWS and GCP). FIG. 4A illustrates an example of a routing graph 400 that the controller path-identifying layer 282 produces in some embodiments from the measurement graph 300.

FIG. 5 illustrates a process 500 that the controller path-identifying layer uses to generate a routing graph from a measurement graph received from the controller measurement layer. The path-identifying layer 282 performs this process 500 repeatedly as it repeatedly receives updated measurement graphs from the controller measurement layer (e.g., performs the process 500 each time that it receives a new measurement graph, or each N^thtime that it receives a new measurement graph). In other embodiments, the path-identifying layer 282 performs this process periodically (e.g., once every 12 hours or 24 hours).

As shown, the path-identifying layer initially defines (at 505) the routing graph to be identical to the measurement graph (i.e., to have the same links between the same pairs of managed forwarding nodes). At 510, the process removes bad links from the measurement graph 300. Examples of bad links are links with excessive message loss or poor reliability (e.g., links with greater than 2% message loss in last 15 minutes, or with message loss greater than 10% in the last 2 minute). FIG. 4A illustrates that links 302, 304 and 306 in the measurement graph 300 are excluded in the routing graph 400. This figure illustrates the exclusion of these links by depicting these links with dashed lines.

Next, at 515, the process 500 computes a link weight score (cost score) as a weighted combination of several computed and provider-specific values. In some embodiments, the weight score is a weighted combination of the link's (1) computed delay value, (2) computed loss value, (3) provider network-connection cost, and (4) provider compute cost. In some embodiments, the provider compute cost is accounted for as the managed forwarding nodes connected by the link are machines (e.g., VMs or containers) that execute on host computers in the public cloud datacenter(s).

At 520, the process adds to the routing graph the known source and destination IP addresses (e.g., known IPs of SaaS providers used by the corporate entity) for the data message flows in the virtual network. In some embodiments, the process adds each known IP address of a possible message-flow endpoint to the node (e.g., to the node representing an MFN) in the routing graph that is closest to that end point. In doing so, the process in some embodiments assumes that each such endpoint is connected to the virtual network through a link with a zero delay cost and a zero loss cost. FIG. 4B illustrates an example of adding known IPs for two SaaS providers to the two nodes 402 and 404 (representing two MFNs) in the routing graph that are in datacenters that are closest to the datacenters of these SaaS providers. In this example, one node is in an AWS public cloud, while the other node is in the GCP public cloud.

Alternatively, or conjunctively, the process 500 in some embodiments adds the known source and destination IP addresses to the routing graph by adding nodes to this graph to represent the source and destination endpoints, assigning IP addresses to these nodes, and assigning weight values to the links that connect these added nodes to other nodes in the routing graph (e.g., to nodes in the routing graph that represent MFNs in the public clouds). When the source and destination endpoints for the flows are added as nodes, the path-identifying engine 282 can account for cost (e.g., distance cost, delay cost, and/or financial cost, etc.) of reaching these nodes when it is identifying different routes through the virtual network between different source and destination endpoints.

FIG. 4C illustrates a routing graph 410 that is generated by adding two nodes 412 and 414 to the node graph 400 of FIG. 4A in order to represent two SaaS providers. In this example, the known IP addresses are assigned to nodes 412 and 414, and these nodes are connected to nodes 402 and 404 (representing two MFNs) through links 416 and 418 that have weights W1 and W2 assigned to them. This approach is an alternative approach for adding the known IP addresses of the two SaaS providers to the approach illustrated in FIG. 4B.

FIG. 4D illustrates a more detailed routing graph 415. In this more detailed routing graph, additional nodes 422 and 424 are added to represent external corporate compute nodes (e.g., branch offices and datacenters) with known IP addresses that connect respectively to the AWS and GCP public clouds 310 and 320. Each of these nodes 422/424 is connected by at least one link 426 with an associated weight value Wi to at least one of the routing graph nodes that represents an MFN. Some of these nodes (e.g., some of the branch offices) are connected with multiple links to same MFN or to different MFNs.

Next, at 525, the process 500 compute the lowest cost paths (e.g., shortest paths, etc.) between each MFN and each other MFN that can serve as a virtual network egress location for a data message flow of the corporate entity. The egress MFNs in some embodiments include the MFNs connected to external corporate compute nodes (e.g., branch offices, corporate datacenters, and SaaS provider datacenters) as well as MFNs that are candidate locations for mobile device connections and egress Internet connections. In some embodiments, this computation uses a traditional lowest-cost (e.g., shortest-path) identification process that identifies the shortest paths between different MFN pairs.

For each candidate MFN pair, the lowest-cost identification process uses the computed weight scores (i.e., the scores computed at 510) to identify a path with the lowest score when multiple such paths exist between the MFN pair. Several manners for computing lowest-cost paths will be further described below. As mentioned above, the path-identifying layer 282 identifies multiples paths between two MFN pairs in some embodiments. This is to allow the cloud forwarding elements 235 to use different paths under different circumstances. Accordingly, in these embodiments, the process 500 can identify multiple paths between two MFN pairs.

At 530, the process removes from the routing graph the links between MFN pairs that are not used by any of the lowest-cost paths identified at 525. Next, at 535, the process generates the routing tables for the cloud forwarding elements 235 from the routing graph. At 535, the process distributes these routing tables to the cloud forwarding elements 235 of the managed forwarding nodes. After 535, the process ends.

In some embodiments, the virtual network has two types of external connections, which are: (1) external secure connections with the compute nodes (e.g., branch offices, datacenters, mobile users, etc.) of an entity, and (2) external connections to third party computers (e.g., SaaS provider servers) through the Internet. Some embodiments optimize the virtual network by finding optimal virtual-network ingress and egress locations for each datapath that terminates at source and destination nodes outside of the virtual network. For instance, to connect a branch office to a SaaS provider server (e.g., salesforce.com server), some embodiments connect the branch office to an optimal edge MFN (e.g., the MFN that has the fastest network connection to the branch office or the one that is closest to the branch office), and identify an optimal edge MFN to an optimally located SaaS provider server (e.g., the SaaS that is closest to the edge MFN for the branch office or has the fastest path to the edge MFN for the branch office through the edge MFN connected to the SaaS provider server).

To associate each compute node (e.g., a branch office, a mobile user, etc.) of an entity to the closest MFN through a VPN connection, the virtual network provider in some embodiments deploys one or more authoritative domain name servers (DNS) in the public clouds for the compute nodes to contact. In some embodiments, each time a corporate compute node in some embodiments needs to establish a VPN connection (i.e., to initialize or re-initialize the VPN connection) to an MFN of the virtual network provider, the compute node first resolves an address associated with its virtual network (e.g., virtualnetworkX.net) with this authoritative DNS server in order to obtain from this server the identity of the MFN that this server identifies as the MFN that is closest to the corporate compute node. To identify this MFN, the authoritative DNS server provides an MFN identifier (e.g., the IP address of the MFN) in some embodiments. The corporate compute node then establishes a VPN connection to this managed forwarding node.

In other embodiments, the corporate compute node does not first perform a DNS resolution (i.e., does not first resolve a network address for a particular domain) each time that it needs to establish a VPN connection to an MFN of the VNP. For instance, in some embodiments, the corporate compute node sticks with a DNS-resolved MFN for a particular duration (e.g., for a day, a week, etc.) before performing another DNS resolution to determine whether this MFN is still an optimal one to which is should connect.

When the source IP address in the DNS request is that of the local DNS server of the corporate compute node, and not of the node itself, the authoritative DNS server in some embodiments identifies the MFN closest to the local DNS server instead of the MFN closest to the corporate compute node. To address this, the DNS request in some embodiments identifies the corporate compute node in terms of a domain name that includes one or more parts (labels) that are concatenated and delimited by dots, where one of these parts identifies the corporation and the other part identifies the compute node of the corporation.

In some embodiments, this domain name specifies a hierarchy of domains and sub-domains that descends from the right label to the left label in the domain name. The right-most first label identifies the particular domain, a second label to the left of the first label identifies the corporate entity, and a third label to the left of the second label identifies the external machine location of the entity in cases where the entity has more than one external machine location. For instance, in some embodiments, the DNS request identifies the corporate compute node as myNode of company myCompany, and asks for the resolution of the address myNode.myCompany.virtualnetwork.net. The DNS server then uses the myNode identifier to better select the ingress MFN to which the corporate compute node should establish a VPN connection. In different embodiments, the myNode identifier is expressed differently. For example, it may be addressed as an IP address, a latitude/longitude description of a location, a GPS (Global Positioning System) location, a street address, etc.

Even when the IP address properly reflects the location, there may be several potential ingress routers, e.g., belonging to different datacenters in the same cloud or to different clouds in the same region. In such a case, the virtual network authoritative server in some embodiments sends back a list of IPs of potential MFN CFEs (e.g., C5, C8, C12). The corporate compute node in some embodiments then pings the different CFEs in the list, to produce measurements (e.g., distance or speed measurements), and selects the closest one by comparing measurements among the set of CFE candidates.

In addition, the corporate compute node may base this selection by identifying the MFNs currently used by the other compute nodes of the corporate entity. For example, in some embodiments, the corporate compute node adds connection costs to each MFN, so that if many of the corporate branches are already connected to a given cloud, new compute nodes would have an incentive to connect to the same cloud, thus minimizing inter-cloud costs in terms of processing, latency, and dollars.

Other embodiments use other DNS resolution techniques. For instance, each time a corporate compute node (e.g., a branch office, datacenter, a mobile user, etc.) needs to perform a DNS resolution, the corporate compute node (e.g., the mobile device or a local DNS resolver at a branch office or datacenter) communicates with a DNS service provider that serves as an authoritative DNS resolver for a number of entities. In some embodiments, this DNS service provider has DNS resolving machines located in one or more private datacenters, while in other embodiments it is part of one or more public cloud datacenters.

To identify which of N managed forwarding nodes that connect directly to the Internet should be used to reach a SaaS provider server, the virtual network (e.g., the ingress MFN or the controller cluster that configures the MFNs) in some embodiments identifies a set of one or more candidate edge MFNs from the N managed forwarding nodes. As described further below, each candidate edge MFN in some embodiments is an edge MFN that is deemed to be optimal based on a set of criteria, such as distance to SaaS provider server, network connection speed, cost, delay and/or loss, network compute cost, etc.

To assist in identifying the optimal edge points, the controller cluster of some embodiments maintains for an entity a list of the most popular SaaS providers and consumer web destinations and their IP address subnets. For each such destination, the controller cluster assigns one or more of the optimal MFNs (again as judged by physical distance, network connection speed, cost, loss and/or delay, compute cost, etc.) as candidate egress nodes. For each candidate egress MFN, the controller cluster then computes the best route from each possible ingress MFN to the candidate MFN, and sets up the resulting next-hop table in the MFNs accordingly, such that the Internet SaaS provider or web destination is associated to the correct virtual network next-hop node.

Given that the service destination can often be reached through several IP subnets at several locations (as provided by the authoritative DNS server), there are several potential egress nodes to minimize latency and provide load-balancing. Accordingly, in some embodiments, the controller cluster computes the best location and egress node for each MFN, and updates the next-hop accordingly. Also, the best egress node to get to a SaaS provider (e.g., office365.com) may be through one public cloud provider (e.g., Microsoft Azure), but the best ingress MFN from purely a distance or connection speed may be in another public cloud provider (e.g., AWS). In such situations, it may not be optimal in terms of latency, processing and cost to traverse to another cloud (i.e., to the public cloud with the best egress MFN) before leaving the virtual network. Providing multiple candidate edge nodes would allow for the selection of an optimal edge MFN and an optimal path to the selected edge MFN in such situations.

To identify the optimal path through the virtual network to an egress MFN that connects to the Internet or connects to a corporate compute node of the corporate entity, the controller cluster identifies optimal routing paths between the MFNs. As mentioned above, the controller cluster in some embodiments identifies the best path between any two MFNs by first costing each link between a pair of directly connected MFNs, e.g., based on a metric score that reflects the weighted sum of estimated latency and financial costs. The latency and financial costs include in some embodiments (1) link delay measurements, (2) estimated message processing latency, (3) cloud charges for outgoing traffic from a particular datacenter either to another datacenter of the same public cloud provider, or to exit the public cloud (PC) provider's cloud (e.g., to another public cloud datacenter of another public cloud provider or to the Internet), and (4) estimated message processing costs associated with the MFNs executing on host computers in the public clouds.

Using the computed costs of these pair-wise links, the controller cluster can compute the cost of each routing path that uses one or more of these pair-wise links by aggregating the costs of the individual pair-wise links that are used by the routing path. As described above, the controller cluster then defines its routing graph based on the computed costs of the routing paths, and generates the forwarding tables of the cloud routers of the MFNs based on the defined routing graphs. Also, as mentioned above, the controller cluster repeatedly performs these costing, graph-building, and forwarding table update and distribution operations periodically (e.g., once every 12 hours, 24 hours, etc.) or as it receives measurement updates from the measurement agents of the MFNs.

Whenever the forwarding table at an MFN CFE C_ipoints to a next-hop MFN CFE C_j, the CFE C_iconsiders C_jas a neighbor. In some embodiments, the CFE C_iestablishes a secure, actively maintained VPN tunnel to CFE C_j. A secure tunnel in some embodiments is a tunnel that requires the payloads of the encapsulated data messages to be encrypted. Also, in some embodiments, a tunnel is actively maintained by one or both endpoints of the tunnel sending keep alive signals to the other endpoint.

In other embodiments, the CFEs do not establish secure, actively maintained VPN tunnels. For instance, in some embodiments, the tunnels between the CFEs are static tunnels that are not actively monitored through the transmission of keep-alive signals. Also, in some embodiments, these tunnels between the CFEs do not encrypt their payloads. In some embodiments, the tunnels between pair of CFEs include two encapsulating headers, with the inner header identifying the tenant ID and the ingress and egress CFEs for a data message entering and exiting the virtual network (i.e., entering and exiting the public cloud(s)), and the outer encapsulating header specifying the source and destination network addresses (e.g., IP addresses) for traversing through zero or more CFE from the ingress CFE to the egress CFE.

In addition to internal tunnels, the virtual network in some embodiments connects corporate compute nodes to their edge MFNs using VPN tunnels, as mentioned above. Therefore, in the embodiments where secure tunnels are used to connect the CFEs, the data messages transit through virtual network using an entirely secure VPN path.

As the virtual network data messages are forwarded using encapsulation within the virtual network, the virtual network in some embodiments uses its own unique network addresses that are different than the private addresses used by the different private networks of the tenant. In other embodiments, the virtual network uses the private and public network address spaces of the public clouds over which it is defined. In yet other embodiments, the virtual network uses some of its own unique network addresses for some of its components (e.g., some of its MFNs, CFEs, and/or services), while using the private and public network address spaces of the public clouds for other of its components.

Also, in some embodiments, the virtual network uses a clean-slate communication platform with its own proprietary protocols. In the embodiments in which the data messages are forwarded entirely through software MFN routers (e.g., through software CFEs), the virtual network can provide an optimized rate control for long-haul end-to-end connections. This is accomplished in some embodiments by operating a TCP optimization proxy engine 220 at every MFN 150. In other embodiments that do not break the TCP itself (e.g., with HTTPS), this is accomplished by the proxy engine 220 segmenting the rate control using intermediate per-flow buffering together with TCP receiver-window and ACK manipulation.

Due to its clean-slate nature, the virtual network in some embodiments optimizes many of its components to provide an even better service. For instance, in some embodiments, the virtual network uses multiple-path routing to support premium bandwidth-guaranteed VPN setups that are routed across the virtual network. In some embodiments, such VPNs include state data in each MFN similar to ATM/MPLS routing, and their establishment and removal is centrally controlled. Some embodiments identify the available bandwidth per outgoing link, either by measuring it directly (through packet pair or a similar process) or by having a given capacity for the link and reducing from this capacity the traffic that is already sent through this link.

Some embodiments use the residual bandwidth of a link as a constraint. For instance, when a link does not have at least 2 Mbps of available bandwidth, the controller cluster of some embodiments removes the link from the set of links that are used to compute lowest-cost path (e.g., shortest path) to any destination (e.g., remove the link from the routing graph, such as graph 400). If an end-to-end route is still available after the removal of this link, new VPNs will be routed across this new route. VPN removal can bring back available capacity to a given link, which in turn can enable this link to be included in the lowest-cost path (e.g., shortest path) calculation. Some embodiments use other options for multiple-path routing such as load balancing of traffic across multiple paths, e.g., using MPTCP (multi-path TCP).

Some embodiments provide a better service for premium customers by exploiting the path parallelism and the inexpensive cloud links to duplicate traffic from the ingress MFNs to the egress MFN, through two disjoint paths (e.g., maximally disjoint paths) within the virtual network. Under this approach, the earliest message that arrives is accepted, and the later one discarded. This approach increases the virtual network reliability and reduces the delay, at the cost of increasing the egress processing complexity. In some such embodiments, Forward Error Correction (FEC) techniques are used to increase reliability while reducing the duplication traffic. Due to its clean-slate nature, the virtual network of some embodiments performs other upper-layer optimizations, such as application-layer optimizations (e.g., de-duplication and caching operations) and security optimizations (e.g., the addition of encryption, DPI (deep packet inspection) and firewalling).

The virtual network of some embodiments accounts for collaboration with cloud providers, to further improve the virtual network setup by using anycast messaging. For instance, in some embodiments when all MFNs obtain the same external IP address, it is easier to connect any new corporate compute node to an optimal edge node (e.g., the closest edge node) using an anycast connection. Likewise, any SaaS provider can obtain this IP address and connect to the optimal MFN (e.g., closest MFN).

As mentioned above, different embodiments use different types of VPN connections to connect corporate compute nodes (e.g., branches and mobile devices) to the MFNs that establish the virtual network of a corporate entity. Some embodiments use IPsec to set up these VPN connections. FIG. 6 illustrates the IPsec data message format of some embodiments. Specifically, this figure illustrates an original format of a data message 605 generated by a machine at the corporate compute node, and an IPsec encapsulated data message 610 after the data message 605 has been encapsulated (e.g., at the corporate compute node or the MFN) for transmission through an IPsec tunnel (e.g., to the MFN or to the corporate compute node).

In this example, the IPsec tunnel is set up with ESP Tunnel Mode, port 50. As shown, this mode is set up in this example by replacing the TCP protocol identifier in the IP header with an ESP protocol identifier. The ESP header identifies the start of the message 615 (i.e., the header 620 and payload 625). The message 615 has to be authenticated by the recipient of the IPsec encapsulated data message (e.g., by the IPsec gateway of the MFN). The start of the payload 625 is identified by the value of the next field 622 of the message 615. Also, the payload 625 is encrypted. This payload includes the IP header, the TCP header and payload of the original data message 605, as well as a padding field 630, which includes the next field 622.

In some embodiments, each MFN IPsec gateway can handle multiple IPsec connections for the same or different virtual network tenants (e.g., for the same corporation or for different corporations). Accordingly, an MFN IPsec gateway (e.g., gateway 230) in some embodiments identifies each IPsec connection in terms of a tunnel ID, a tenant ID (TID), and a corporate compute node subnet. In some embodiments, different corporate nodes (e.g., different branch offices) of a tenant do not have overlapping IP subnets (per RFC 1579). The IPsec gateway in some embodiments has a table mapping each IPsec tunnel ID (which is contained in the IPsec tunnel header) to a tenant ID. For a given tenant that an IPsec gateway is configured to handle, the IPsec gateway also has a mapping of all subnets of that tenant that connect to the virtual network established by the MFNs and their cloud forwarding elements.

When an ingress first MFN in a first public cloud datacenter receives through an IPsec tunnel a data message associated with a tenant ID and destined to a destination (e.g., a branch or datacenter subnet, or a SaaS provider) that connects to an egress second MFN in a second public cloud datacenter, the IPsec gateway of the first MFN removes the IPsec tunnel header. In some embodiments, the CFE of the first MFN then encapsulates the message with two encapsulating headers that allow the message to traverse a path from the ingress first MFN to the egress second MFN, directly or through one or more other intermediate MFNs. The CFE of the first MFN identifies this path by using its controller-configured routing table.

As mentioned above, the two encapsulating headers in some embodiments include (1) an outer header that specifies the next hop MFN CFE to allow the encapsulated data message to traverse through the MFNs of the virtual network to reach the egress MFN CFE, and (2) an inner header that specifies the tenant ID and the ingress and egress MFN CFEs that identify the MFNs for the data message entering and exiting the virtual network.

Specifically, in some embodiments, the inner encapsulating header includes a valid IP header with the destination IP address of the egress second MFN's CFE and the source IP address of the ingress first MFN's CFE. This approach allows standard IP router software to be used in every CFE of the MFNs. The encapsulation further includes the tenant ID (e.g., a customer CID). When a message arrives at the egress second MFN's CFE, it is decapsulated and sent by the second MFN to its destination (e.g., sent by the second MFN's IPsec gateway to the destination via another IPsec tunnel that is associated with the tenant ID and the destination subnet of the message).

Certain cloud providers prohibit machines from “spoofing” source IP, and/or impose other restrictions for TCP and UDP traffic. To deal with such possible restrictions, some embodiments use the outer header to connect neighboring pairs of MFNs that are used by one or more routes. This header in some embodiments is a UDP header that specifies source and destination IP addresses and the UDP protocol parameters. In some embodiments, the ingress MFN CFE specifies its IP address as the source IP address of the outer header, while specifying the next MFN CFE hop's IP address as the destination IP address of the outer header.

When the path to the egress MFN's CFE includes one or more intermediate MFN CFEs, an intermediate CFE replaces the source IP address in the outer header of the double-encapsulated message that it receives with its IP address. It also uses the destination IP address in the inner header to perform a route lookup in its routing table to identify the destination IP address of the next hop MFN CFE that is on the path to the destination IP address of the inner header. The intermediate CFE then replaces the destination IP address in the outer header with the IP address that it identified through its route table lookup.

When the double encapsulated data message reaches the egress MFN's CFE, the CFE determines that it is the egress node for the data message when it retrieves the destination IP address in the inner header and determines that this destination IP address belongs to it. This CFE then removes the two encapsulating headers from the data message and then sends it to it destination (e.g., through its MFN's IPsec gateway to the destination via another IPsec tunnel that is associated with the tenant ID and the destination IP address or subnet in the data message's original header).

FIG. 7 illustrates an example of the two encapsulating headers of some embodiments, while FIG. 8 presents an example that illustrates how these two headers are used in some embodiments. In the discussion below, the inner header is referred to as the tenant header as it includes the tenant ID along with the identity of the virtual-network ingress/egress nodes connected to the tenant's corporate compute end nodes. The outer header is referred to below as the VN-hop tunnel header because it is used to identify the next hop through the virtual network as the data message traverses a path through the virtual network between ingress and egress MFN CFEs.

FIG. 7 shows a VN-hop tunnel header 705 and a tenant tunnel header 720 encapsulating an original data message 750 with an original header 755 and a payload 760. As shown, the VN-hop tunnel header 705 in some embodiments includes a UDP header 710 and an IP header 715. The UDP header in some embodiments is defined according to a UDP protocol. In some embodiments, the VN-hop tunnel is a standard UDP tunnel, while in other embodiments, this tunnel is a proprietary UDP tunnel. In still other embodiments, this tunnel is a standard or proprietary TCP tunnel. The tunnel header 705 in some embodiments is an encrypted one that encrypts its payload, while in other embodiments it is an unencrypted tunnel.

As further described below, the tunnel header 705 in some embodiments is used to define an overlay VNP network, and is used by each MFN CFE to reach the next hop MFN CFE over the underlay public cloud networks. As such, the IP header 715 of the tunnel header 705 identifies the source and destination IP addresses of the first and second CFEs of the first and second neighboring MFNs connected by the VNP tunnel. In some cases (e.g., when the next hop destination MFN is in a different public cloud of a different public cloud vendor than the source MFN), the source and destination IP addresses are public IP addresses that are used by the public cloud datacenters that include the MFNs. In other cases, when the source and destination MFN CFEs belong to the same public cloud, the source and destination IP addresses can be private IP addresses that are used in just the public cloud. Alternatively, in such cases, the source and destination IP addresses might still be public IP addresses of the public cloud vendor.

As shown in FIG. 7, the tenant tunnel header 720 includes an IP header 725, a tenant ID field 730 and a virtual circuit label (VCL) 735. The tenant tunnel header 720 is used by each hop CFE after the ingress hop CFE to identify the next hop for forwarding the data message to the egress CFE of the egress MFN. As such, the IP header 725 includes a source IP address that is the IP address of the ingress CFE and a destination IP address that is the IP address of the egress CFE. As with the source and destination IP addresses of the VN-hop header 705, the source and destination IP addresses of the tenant header 720 can be either private IP addresses of one public cloud provider (when the data message traverses a route that only goes through one public cloud provider's datacenter), or public IP addresses of one or more public cloud providers (e.g., when the data message traverses a route that goes through datacenters of two or more public cloud providers).

The IP header of the tenant header 720 can be routed by using any standard software router and IP routing table in some embodiments. The tenant ID field 730 contains the tenant ID, which is a unique tenant identifier that can be used at the ingress and egress MFNs to uniquely identify a tenant. The virtual network provider in some embodiments defines different tenant IDs for different corporate entities that are tenants of the provider. The VCL field 735 is an optional routing field that some embodiments use to provide an alternative way (non-IP based way) for forwarding messages through the network. In some embodiments, the tenant tunnel header 720 is a GUE (Generic UDP Encapsulation) header.

FIG. 8 presents an example that illustrates how these two tunnel headers 705 and 710 are used in some embodiments. In this example, a data messages 800 is sent from a first machine 802 (e.g., first VM) in a first branch office 805 of a company to a second machine 804 (e.g., a second VM) in a second branch office 810 of the company. The two machines are in two different subnets, which are 10.1.0.0 and 10.2.0.0, with the first machine having an IP address 10.1.0.17 and the second machine having an IP address 10.2.0.22. In this example, the first branch 805 connects to an ingress MFN 850 in a first public cloud datacenter 830, while the second branch 810 connects to an egress MFN 855 in a second public cloud datacenter 838. Also, in this example, the ingress and egress MFNs 850 and 855 of the first and second public cloud datacenters are indirectly connected through an intermediate MFN 857 of a third public cloud datacenter 836.

As shown, the data message 800 from machine 802 is sent to the ingress MFN 850 along an IPsec tunnel 870 that connects the first branch office 805 to the ingress MFN 850. This IPsec tunnel 870 is established between an IPsec gateway 848 of the first branch office and an IPsec gateway 852 of the ingress MFN 850. This tunnel is established by encapsulating the data message 800 with an IPsec tunnel header 806.

The IPsec gateway 852 of the MFN 850 decapsulates the data message (i.e., removes the IPsec tunnel header 806), and passes the decapsulated message to this MFN's CFE 832 directly or through one or more middlebox service machines (e.g., through a firewall machine, such as machine 210 of FIG. 2). In passing this message, the IPsec gateway or some other module of the MFN 850 in some embodiments associates the message with the tunnel ID of the IPsec tunnel and a tenant ID of the company. This tenant ID identifies the company in the records of the virtual network provider.

Based on the associated tenant ID and/or the IPsec tunnel ID, the CFE 832 of the ingress MFN 850 identifies a route for the message to its destination machine's subnet (i.e., to the second branch office 810) through the virtual network that is established by the MFNs in the different public cloud datacenters. For instance, the CFE 832 uses the tenant ID and/or the IPsec tunnel ID to identify the routing table for the company. In this routing table, the CFE 832 then uses the destination IP address 10.2.0.22 of the received message to identify a record that identifies the CFE 853 of the egress MFN 855 of the public cloud datacenter 838 as the destination egress forwarding node for the data message 800. In some embodiments, the identified record maps the entire subnet 10.2.0.0/16 of the second branch office 810 to the CFE 853 of the MFN 855.

After identifying the egress CFE 853, the CFE 832 of the ingress MFN 850 encapsulates the received data message with a tenant tunnel header 860 that in its IP header 725 includes the source IP of the ingress CFE 832 and the destination IP of the egress CFE 853. In some embodiments, these IP addresses are defined in the public IP address space. The tunnel header 860 also includes the tenant ID that was associated with the data message at ingress MFN 850. As mentioned above, this tunnel header also includes the VCL header value in some embodiments.

In some embodiments, the ingress CFE 832 also identifies the next hop MFN that is on the desired CFE routing path to the egress CFE 853. In some embodiments, the ingress CFE 832 identifies this next hop CFE in its routing table by using the destination IP address of the egress CFE 853. The next hop MFN CFE in this example is the CFE 856 of the third MFN 857 of a third public cloud datacenter 836.

After identifying the next hop MFN CFE, the ingress MFN CFE encapsulates the encapsulated data message 800 with a VN-hop, second tunnel header 862. This tunnel header allows the message to route to the next hop CFE 856. In the IP header 715 of this outer header 862, ingress MFN CFE 832 specifies the source and destination IP addresses as the source IP of the ingress CFE 832 and the destination IP of the intermediate CFE 856. It also specifies its layer 4 protocol as being UDP in some embodiments.

When the CFE 856 of the third MFN 857 receives the double-encapsulated data message, it removes the VN-hop, second tunnel header 862, and the extracts from the tenant header 860 the destination IP address of the CFE 853 of the egress MFN 855. Since this IP address is not associated with the CFE 856, the data message still has to traverse to another MFN to reach its destination. Accordingly, the CFE 856 uses the extracted destination IP address to identify a record in its routing table that identifies the next hop MFN CFE 853. It then changes re-encapsulates the data message with the outer header 705 and specifies the source and destination IP addresses in its IP header 715 as its own IP address and the destination IP address of the MFN CFE 853. Next, the CFE 856 forwards the double-encapsulated data message 800 to the egress CFE 853 through intervening routing fabric of the public cloud datacenters 836 and 838.

After receiving the encapsulated data message, the egress CFE 853 determines that the encapsulated message is directed to it when it retrieves the destination IP address in the inner header 860 and determines that this destination IP address belongs to it. The egress CFE 853 removes both encapsulating headers 860 and 862 from the data message 800, and extracts the destination IP address in the data message's original header. This destination IP address identifies the IP address of the second machine 804 in the second branch office's subnet.

Using the tenant ID in the removed tenant tunnel header 860, the egress CFE 853 identifies the correct routing table to search, and then searches this routing table based on the destination IP address extracted from the original header value of the received data message. From this search, the egress CFE 853 identifies a record that identifies the IPsec connection to use to forward the data message to its destination. It then provides the data message along with the IPsec connection identifier to the second MFN's IPsec gateway 858, which then encapsulates this message with an IPsec tunnel header 859 and then forwards it to an IPsec gateway 854 of the second branch office 810. The gateway 854 then removes the IPsec tunnel header and forwards the data message to its destination machine 804.

Several more detailed message-processing examples will now be described by reference to FIGS. 9-15. In these examples, it is assumed that each tenant IPsec interface is on the same local public IP address, as are the VNP tunnels. As such, the interfaces in some embodiments are attached to a single VRF (virtual routing and forwarding) namespace. This VRF namespace is referred to below as the VNP namespace.

FIGS. 9-11 illustrate message-handling processes 900-1100 that are performed respectively by the ingress, intermediate, and egress MFNs when they receive a message that is sent between two compute devices in two different external machine locations (e.g., branch offices, datacenters, etc.) of a tenant. In some embodiments, the controller cluster 160 configures the CFE of each MFN to operate as an ingress, intermediate, and egress CFE, when each such CFE is a candidate to serve as an ingress, intermediate and egress CFE for different data message flows of a tenant.

The processes 900-1100 will be explained below by reference to two examples in FIGS. 8 and 12. As mentioned above, FIG. 8 illustrates an example when the data message goes through an intermediate MFN to get to the egress MFN. FIG. 12 illustrates an example that does not involve an intermediate MFN between the ingress and egress MFNs. Specifically, it illustrates a data message 1200 being sent from a first device 1202 in a first branch office 1205 to a second device 1210 in a second branch office 1220 when the two branch offices connect to two public cloud datacenters 1230 and 1238 with two MFNs 1250 and 1255 that are directly connected. As shown, the CFEs 1232 and 1253 of the MFNs in these examples perform the routing operations associated with each MFN.

The ingress CFE (e.g., ingress CFE 832 or 1232) of the ingress MFNs 850 and 1250 perform the process 900 in some embodiments. As shown in FIG. 9, the ingress process 900 starts by initially identifying (at 905) the tenant routing context based on the identifier of the IPsec tunnel (e.g., 806 or 1206) in the received data message. In some embodiments, the IPsec gateways or other MFN modules store the tenant IDs for the IPsec tunnel IDs in mapping tables. Whenever a data message is received along a particular IPsec tunnel, the IPsec gateway extracts the IPsec tunnel ID, which this gateway or another MFN module then uses to identify the associated tenant ID by reference to its mapping table. By identifying the tenant ID, the process identifies the tenant routing table or the tenant portion of the VRF namespace to use.

At 910, the process increments the identified IPsec tunnel's RX (receive) counter to account for receiving this data message. Next, at 915, the process performs a route lookup (e.g., a longest prefix match, LPM, lookup) in the identified tenant routing context (e.g., in the tenant's portion of the VRF namespace) to identify the IP address of the egress interface for exiting the tenant's virtual network that is built over the public cloud datacenters. For the branch-to-branch examples, the egress interface is the IP address of an egress CFE (e.g., CFE 853 or 1253) of an MFN connected to the destination branch.

At 920, the process adds a tenant tunnel header (e.g., header 860 or 1260) to the received data message, and embeds the source IP address of the ingress CFE (e.g., ingress CFE 832 or 1252) and the destination IP address of the egress CFE (e.g., egress CFE 853 or 1253) as the source and destination IP addresses in this tunnel header. In the tenant header, the process also stores the tenant ID (identified at 905) in the tenant header. At 920, the process adds a VN-hop tunnel header (e.g., header 862 or 1262) outside of the tenant header, and stores its IP address as the source IP address in this header. The process also specifies (at 920) the UDP parameters (e.g., UDP port) of the VNP tunnel header.

Next, at 925, the process increments the VN-transmit counter for the tenant to account for this data message's transmission. At 930, the process performs a route lookup (e.g., an LPM lookup) in the identified VNP routing context (e.g., in the VNP's portion of the VRF namespace) to identify the next hop interface for this data message. In some embodiments, this route lookup is an LPM lookup (e.g., in the VNP's portion of the VRF namespace) that is at least partially based on the egress CFE's destination IP.

At 935, the process determines whether the next hop egress interface is a local interface (e.g., a physical or virtual port) of the ingress CFE. If so, the process defines (at 937) the destination IP address in the VN-hop outer tunnel header as the egress interface IP address identified at 915. Next, at 940, the process provides the double encapsulated data message to its local interface so that it can be forwarded to the destination egress CFE. After 940, the process 900 ends.

FIG. 12 illustrates an example of the operation 905-940 for the data message 1200 that the ingress CFE 1232 receives from the device 1202 of the first branch office 1205. As shown, this CFE's MFN 1250 receives this data message as an IPsec encapsulated message at its IPsec gateway 1252 from the IPsec gateway 1248 of the first branch office 1205. The ingress CFE 1232 encapsulates the received message 1200 (after its IPsec header has been removed by an IPsec gateway 1252) with a VN-hop tunnel header 1262 and a tenant tunnel header 1260, and forwards this double encapsulated message to the egress CFE 1253 of MFN 1255 of public cloud 1238. As shown, the source and destination IP addresses of both tunnel headers 1260 and 1262 are identical in this example. Given that these two sets of IP addresses are identical, some embodiments forego using the outer IP header 1262 when the data message is not routed through any intervening CFE, such as CFE 856.

When the process determines (at 935) that the next hop egress interface is not a local interface of the ingress CFE but rather is the destination IP address of another router, the process embeds (at 945) in the VN-hop tunnel header, the destination IP address of the next hop intermediate CFE (e.g., intermediate CFE 856) as the destination IP address of the VN-hop tunnel header.

Next, at 950, the process performs another route lookup (e.g., an LPM lookup) in the identified VNP routing context (e.g., in the VNP's portion of the VRF namespace). This time, the lookup is based on the IP address of the intermediate CFE that is identified in the VNP tunnel header. As the intermediate CFE (e.g., CFE 856) is a next-hop CFE in the virtual network for the ingress CFE (e.g., CFE 832), the routing table identifies a local interface (e.g., a local port) for data messages sent to the intermediate CFE. Thus, this lookup in the VNP routing context identifies a local interface, to which the ingress CFE provides (at 950) the double-encapsulated message. The process then increments (at 955) the VN-intermediate counter to account for this data message's transmission. After 955, the process ends.

FIG. 10 illustrates a process 1000 that a CFE (e.g., CFE 853 or 1253) of an egress MFN performs in some embodiments when it receives a data message that should be forwarded to a corporate compute node (e.g., a branch office, datacenter, remote user location) connected to the MFN. As shown, the process initially receives (at 1005) the data message on an interface associated with the virtual network. This message is encapsulated with the VN-hop tunnel header (e.g., header 862 or 1262) and tenant tunnel header (e.g., header 860 or 1260).

At 1010, the process determines that the destination IP address in the VN-hop tunnel header is its CFE's destination IP address (e.g., IP address of CFE 853 or 1253). Next, at 1015, the process removed the two tunnel headers. The process then retrieves (at 1020) the tenant ID from the removed tenant tunnel header. To account for the received data message, the CFE then increments (at 1025) the RX (receive) counter that it maintains for the tenant specified by the extracted tenant ID.

Next, at 1030, the process performs a route lookup (e.g., an LPM lookup) in the identified tenant routing context (i.e., in the routing context of the tenant identified by the tenant ID extracted at 1020) to identify the next hop interface for this data message. The process performs this lookup based on the destination IP address in the original header (e.g., header 755) of the received data message in some embodiments. From the record identified through this lookup, the process 1000 identifies the IPsec interface through which the data message has to be sent to its destination. Accordingly, the process 1000 sends the decapsulated, received data message to its MFN's IPsec gateway (e.g., gateway 858 or 1258).

This gateway then encapsulates the data message with an IPsec tunnel header (e.g., tunnel header 859 or 1259) and forwards it to a gateway (e.g., gateway 854 or 1254) in the destination corporate compute node (e.g., destination branch office), where it will be decapsulated and forwarded to its destination. After 1030, the CFE or its MFN increments (at 1035) the counter that it maintains for transmitting messages along the IPsec connection to the destination corporate compute node (e.g., the IPsec connection between gateways 854 and 858, or between gateways 1254 and 1258).

FIG. 11 illustrates a process 1100 that a CFE (e.g., CFE 856) of an intermediate MFN performs in some embodiments when it receives a data message that should be forwarded to another CFE of another MFN. As shown, the process initially receives (at 1105) the data message on an interface associated with the virtual network. In some embodiments, this message is encapsulated with two tunnel headers, a VN-tunnel header (e.g., header 862) and a tenant tunnel header (e.g., header 860).

At 1110, the process terminates the VN-hop tunnel as it determines that the destination IP address in this tunnel header is its CFE's destination IP address (e.g., is the destination IP address of CFE 856). Next, at 1115, the process determines whether the VN-hop tunnel header specifies the correct UDP port. If not, the process ends. Otherwise, at 1120, the process removes the VN-hop tunnel header. To account for the received data message, the CFE then increments (at 1125) the RX (receive) counter that it maintains to quantify the number of messages that it has received as an intermediate hop CFE.

At 1130, the process performs a route lookup (e.g., an LPM lookup) in the identified VNP routing context (e.g., in the VNP's portion of the VRF namespace) to identify the next hop interface for this data message. In some embodiments, this route lookup is an LPM lookup (e.g., in the VNP's portion of the VRF namespace) that is at least partially based on the egress CFE's destination IP that is identified in the inner tenant tunnel header.

The process then determines (at 1135) whether the next hop egress interface is a local interface of the intermediate CFE. If so, the process adds (at 1140) the VN-hop tunnel header to the data message, which is already encapsulated with the tenant tunnel header. The process sets (at 1142) the destination IP address in the VN-hop tunnel header to the egress CFE's destination IP address that is specified in the tenant tunnel header. It also sets (at 1142) the source IP address in the VN-hop tunnel header to the IP address of its CFE. In this tunnel header, the process also sets the UDP attributes (e.g., the UDP port, etc.).

Next, at 1144, the process provides the double encapsulated data message to its local interface (identified at 1130) so that it can be forwarded to the destination egress CFE. One example of this VN-hop tunnel de-capsulation and forwarding was described above by reference to the operations of CFE 856 in FIG. 8. To account for the received data message, the CFE then increments (at 1146) the TX (transmit) counter that it maintains to quantify the number of messages that it has transmitted as an intermediate hop CFE. After 1146, the process 1100 ends.

On the other hand, when the process determines (at 1135) that the next hop egress interface is not a local interface of its CFE but rather is the destination IP address of another router, the process adds (at 1150) a VN-hop tunnel header to the data message from which it previously removed a VN-hop tunnel header. In the new VN-hop tunnel header, the process 1100 embeds (at 1150) the source IP address of its CFE and the destination IP address (identified at 1130) of the next hop intermediate CFE as the source and destination IP addresses of the VN-hop tunnel header. This VNP tunnel header also specifies a UDP layer 4 protocol with a UDP destination port.

Next, at 1155, the process performs another route lookup (e.g., an LPM lookup) in the identified VNP routing context (e.g., in the VNP's portion of the VRF namespace). This time, the lookup is based on the IP address of the next hop intermediate CFE that is identified in the new VN-hop tunnel header. As this intermediate CFE is a next-hop of the current intermediate CFE in the virtual network, the routing table identifies a local interface for data messages sent to the next-hop intermediate CFE. Thus, this lookup in the VNP routing context identifies a local interface, to which the current intermediate CFE provides the double-encapsulated message. The process then increments (at 1160) the VN-intermediate TX (transmit) counter to account for this data message's transmission. After 1160, the process ends.

FIG. 13 illustrates a message-handling process 1300 that is performed by the CFE of the ingress MFN when it receives a message for a tenant that is sent from a corporate compute device of the tenant (e.g., in a branch office) to another tenant machine (e.g., in another branch office, tenant datacenter or a SaaS provider datacenter). The process 900 of FIG. 9 is a subset of this process 1300 as further described below. As shown in FIG. 13, the process 1300 starts by initially identifying (at 905) the tenant routing context based on the identifier of the incoming IPsec tunnel.

At 1310, the process determines whether both the source and destination IP addresses in the received data message's header are public IP addresses. If so, the process (at 1315) drops the data message and increments the drop counter that it maintains for the received data message's IPsec tunnel. At 1315, the process drops the counter because it should not be receiving messages that are addressed to and from public IP addresses when it receives the messages through the tenant's IPsec tunnel. In some embodiments, the process 1300 also sends back to the source corporate compute machine an ICMP error message.

On the other hand, when the process determines (at 1310) that the data message is not coming from a public IP address and going to another public IP address, the process determines (at 1320) whether the destination IP address in the received data message's header is a public IP address. If so, the process transitions to 1325 to perform process 900 of FIG. 9, with the exception of operation 905, which it has performed at the start of the process 1300. After 1325, the process 1300 ends. On the other hand, when the process 1300 determines (at 1320) that the destination IP address in the received data message's header is not a public IP address, the process increments (at 1330) the identified IPsec tunnel's RX (receive) counter to account for receiving this data message.

The process 1300 then performs (at 1335) a route lookup (e.g., an LPM lookup) in the identified tenant routing context (e.g., in the tenant's portion of the VRF namespace). This lookup identifies the IP address of the egress interface for exiting the tenant's virtual network that is built over the public cloud datacenters. In the example illustrated in FIG. 13, the process 1300 reaches the lookup operation 1335 when the data message is intended for a machine in a SaaS provider datacenter. Hence, this lookup identifies the IP address of the egress router for exiting the tenant's virtual network to reach the SaaS provider machine. In some embodiments, all the SaaS provider routes are installed in one route table or in one portion of the VRF namespace, while in other embodiments the routes for the different SaaS providers are stored in different route tables or different VRF namespace portions.

At 1340, the process adds a tenant tunnel header to the received data message, and embeds the source IP address of the ingress CFE and the destination IP address of the egress router as the source and destination IP addresses in this tunnel header. Next, at 1345, the process increments the VN-transmit counter for the tenant to account for this data message's transmission. At 1350, the process performs a route lookup (e.g., an LPM lookup) in the VNP routing context (e.g., in the VNP's portion of the VRF namespace) to identify one of its local interfaces as the next hop interface for this data message. When the next hop is another CFE (e.g., in other public cloud datacenter), the process in some embodiments further encapsulates the data message with the VN-hop header, and embeds its CFE's IP address and the other CFE's IP address as the source and destination addresses of the VN-hop header. At 1355, the process provides the encapsulated data message to its identified local interface so that the data message can be forwarded to its egress router. After 1355, the process 1300 ends.

In some cases, the ingress MFN can receive a data message for a tenant that its CFE can directly forward to the data message's destination machine without going through another MFN's CFE. In some such cases, the data message does not need to be encapsulated with a tenant header or a VN-hop header when the CFE does not need to relay any tenant specific information to any other subsequent VN processing module or the needed information can be provided to the subsequent VN processing module through other mechanisms.

For instance, to directly forward a tenant's data message to an external SaaS provider datacenter, the ingress MFN's NAT engine 215 would have to perform a NAT operation based on the tenant identifier, as further described below. The ingress CFE or another module in the ingress MFN has to provide the tenant identifier to the ingress MFN's associated NAT engine 215. When the ingress CFE and NAT engines execute on the same computer, some embodiments share this information between these two modules by storing it in a shared memory location. On the other hand, when the CFE and NAT engines do not execute on the same computer, some embodiments use other mechanisms (e.g., an out-of-band communication) to share the tenant ID between the ingress CFE and NAT engines. In such cases, however, other embodiments use an encapsulating header (i.e., use an in-band communication) to store and share the tenant ID between different modules of the ingress MFN.

As further described below, some embodiments perform one or two source NAT operations on the source IP/port addresses of a data message before sending the message outside of the virtual network of a tenant. FIG. 14 illustrates the NAT operation being performed at the egress router. However, as further described below, some embodiments also perform another NAT operation on the data message at the ingress router, even though this extra NAT operation was not described above by reference to FIG. 13.

FIG. 14 illustrates a process 1400 that an egress router performs in some embodiments when it receives a data message that should be forwarded to a SaaS provider datacenter through the Internet. As shown, the process initially receives (at 1405) the data message on an interface associated with the virtual network. This message is encapsulated with the tenant tunnel header.

At 1410, the process determines that the destination IP address in this tunnel header is its router's destination IP address, and hence it removes the tenant tunnel header. The process then retrieves (at 1415) the tenant ID from the removed tunnel header. To account for the received data message, the process increments (at 1420) the RX (receive) counter that it maintains for the tenant specified by the extracted tenant ID.

Next, at 1425, the process determines whether the destination IP in the data message's original header is a public one that is reachable through a local interface (e.g., local port) of the egress router. This local interface is an interface that is not associated with a VNP tunnel. If not, the process ends. Otherwise, the process performs (at 1430) a source NAT operation to change the source IP/port addresses of the data message in this message's header. The NAT operation and the reason for performing it will be further described below by reference to FIGS. 16 and 17.

After 1430, the process performs (at 1435) a route lookup (e.g., an LPM lookup) in the Internet routing context (i.e., in the Internet routing portion of the routing data, e.g., Internet VRF namespace of the router) to identify the next hop interface for this data message. The process performs this lookup based on the destination network address (e.g., destination IP address) of the original header of the received data message in some embodiments. From the record identified through this lookup, the process 1400 identifies the local interface through which the data message has to be sent to its destination. Accordingly, at 1435, the process 1400 provides the source network-address translated data message to its identified local interface for forwarding to its destination. After 1435, the process increments (at 1440) the counter that it maintains for transmitting messages to the SaaS provider, and then ends.

FIG. 15 illustrate a message-handling process 1500 that is performed by the ingress router that receives a message that is sent from a SaaS provider machine to a tenant machine. As shown, the ingress process 1500 starts by initially receiving (at 1505) a data message on a dedicated input interface with a public IP address that is used for several or all SaaS provider communications. In some embodiments, this input interface is a different interface with a different IP address than the one used for communicating with the virtual network.

After receiving the message, the process performs (at 1510) a route lookup in a public Internet routing context by using the destination IP address contained in the received data message's header. Based on this lookup, the process determines (at 1515) whether the destination IP address is local and associated with an enabled NAT operation. If not, the process ends. Otherwise, the process increments (at 1520) the Internet RX (receive) counter to account for receiving the data message.

Next, at 1525, the process performs a reverse NAT operation that translates the destination IP/port addresses of the data message to new destination IP/port addresses that the virtual network associates with a particular tenant. This NAT operation also produces the tenant ID (e.g., retrieves the tenant ID from a mapping table that associates tenant IDs with translated destination IPs, or retrieves the tenant ID from the same mapping table that is used to obtain the new destination IP/port addresses). In some embodiments, the process 1500 uses a connection record that the process 1400 created when it performed (at 1430) its SNAT operation to perform (at 1525) its reverse NAT operation. This connection record contains the mapping between the internal and external IP/port addresses that are used by the SNAT and DNAT operations.

Based on the translated destination network address, the process then performs (at 1530) a route lookup (e.g., an LPM lookup) in the identified tenant routing context (i.e., the routing context specified by the tenant ID) to identify the IP address of the egress interface for exiting the tenant's virtual network and reaching the tenant's machine in a corporate compute node (e.g., in a branch office). This egress interface is the IP address of an egress CFE of an egress MFN in some embodiments. At 1530, the process adds a tenant tunnel header to the received data message, and embeds the IP address of the ingress router and the IP address of the egress CFE as the source and destination IP addresses in this tunnel header. Next, at 1535, the process increments the VN-transmit counter for the tenant to account for this data message's transmission.

At 1540, the process performs a route lookup (e.g., an LPM lookup) in the identified VNP routing context (e.g., in the VNP's portion of the routing data, such as in the VRF namespace of the router) to identify its local interface (e.g., its physical or virtual port), to which the ingress router provides the encapsulated message. The process then adds (at 1540) a VN-hop header to the received data message, and embeds the IP address of the ingress router and the IP address of the next hop CFE as the source and destination IP addresses of this VN-hop header. After 1555, the process ends.

As mentioned above, the MFNs in some embodiments include NAT engines 215 that perform NAT operations on the ingress and/or egress paths of data messages into and out of the virtual network. NAT operations are commonly performed today in many contexts and by many devices (e.g., routers, firewalls, etc.). For instance, a NAT operation is typically performed when traffic exits a private network to isolate the internal IP address space from the regulated, public IP address space used in the Internet. A NAT operation typically maps one IP address to another IP address.

With the proliferation of computers connected to the Internet, the challenge is that the number of computers would exceed the available number of IP Addresses. Unfortunately, even though there are 4,294,967,296 possible unique addresses, it is already not practical to assign a unique public IP address for each computer. One way to get around is to assign public IP addresses only to the routers at the edge point of private networks, while other devices inside the networks get addresses that are only unique in their internal private networks. When a device wants to communicate with a device outside of its internal private network, its traffic typically passes through an Internet gateway that performs a NAT operation to replace the source IP of this traffic with the public source IP address of the Internet gateway.

While a private network's Internet gateway gets a registered public address on the Internet, each device inside of a private network that connects to this gateway receives an unregistered private address. The private addresses of the internal private networks can be in any range of IP addresses. However, the Internet Engineering Task Force (IETF) has suggested several ranges of private addresses for private networks to use. These ranges are generally not available on the public Internet so that routers can easily distinguish between private and public addresses. These ranges of private addresses are known as RFC 1918, and are: (1) Class A 10.0.0.0-10.255.255.255, (2) Class B 172.16.0.0-172.31.255.255, and (3) Class C 192.168.0.0-192.168.255.255.

It is important to perform source IP translation on data message flows exiting private networks, so that external devices can differentiate different devices within different private networks that use the same internal IP addresses. When an external device has to send a reply message to the device inside of a private network, the external device has to send its reply to a unique and routable public address on the Internet. It cannot use the internal device's original IP address that might be used by numerous devices in numerous private networks. The external device sends its reply to the public IP address with which the original NAT operation replaced the private source IP address of the internal device. After receiving this reply message, the private network (e.g., the network's gateway) performs another NAT operation to replace the public destination IP address in the reply with the IP address of the internal device.

Many devices inside of a private network and many applications executing on these devices have to share one or a finite number of public IP address that are associated with the private network. Accordingly, NAT operations typically also translate the layer 4 port addresses (e.g. UDP addresses, TCP addresses, RTP addresses, etc.) to be able to uniquely associate external message flows to internal message flows that start or terminate on different internal machines and/or different applications on these machines. NAT operations are also often stateful operations as in many contexts these operations need to track connections, and dynamically handle tables, message reassembly, timeouts, forced termination of expired tracked connections, etc.

As mentioned above, the virtual network provider of some embodiments provides a virtual network as a service to different tenants over multiple public clouds. These tenants might use common IP addresses in their private networks and they share a common set of network resources (e.g., public IP addresses) of the virtual network provider. In some embodiments, the data traffic of the different tenants is carried between the overlay network's CFEs through tunnels and the tunnel marks each message with a unique tenant ID. These tenant identifiers allow the messages to be sent back to the source devices even when the private tenant IP spaces overlap. For instance, the tenant identifiers allow a message that is sent from a branch office of tenant 17 with source address 10.5.12.1 to Amazon.com to be distinguished from a message sent to Amazon.com from a branch office of tenant 235 with the same source address (and even with the same source port number, 55331).

Standard NATs implemented according to RFC 1631 do not support the notion of tenancy and consequently have no way to distinguish between two messages with the same private IP addresses. However, in many virtual network deployments of some embodiments, it is beneficial to use standard NAT engines as many mature open-source, high-performance implementations exist today. In fact, many Linux kernels today have functioning NAT engines as standard features.

In order to use standard NAT engines for different tenants of tenant virtual networks, the virtual network provider of some embodiments uses tenancy-mapping (TM) engines before using standard NAT engines. FIG. 16 illustrates such TM engines 1605 that are placed in each virtual-network gateway 1602 that is on the virtual network's egress path to the Internet. As shown, each TM engine 1605 is placed before a NAT engine 1610 on the message egress paths to SaaS provider datacenters 1620 through the Internet 1625. In some embodiments, each NAT engine 215 of an MFN includes a TM engine (like the TM engine 1605) and a standard NAT engine (like NAT engine 1610).

In the example illustrated in FIG. 16, the message flows come from two branch offices 1655 and 1660 and a datacenter 1665 of two virtual-network tenants, and enter the virtual network 1600 through the same ingress gateway 1670, although this does not necessarily have to be the case. The virtual network 1600 in some embodiments is defined over multiple public cloud datacenters of multiple public cloud vendors. In some embodiments, the virtual-network gateways are part of the managed forwarding nodes, and the TM engines are placed before the NAT engines 1610 in egress MFNs.

When a data message reaches an egress gateway 1602 to exit the virtual network on its way to a SaaS provider datacenter 1620, each TM engine 1605 maps the source network address (e.g., source IP and/or port addresses) of these data message to new source network address (e.g., source IP and/or port addresses), and the NAT engine 1610 maps the new source network address to yet another source network address (e.g., another source IP and/or port addresses). In some embodiments, the TM engine is a stateless element and performs the mapping for each message through a static table without looking at any dynamic data structure. As a stateless element, the TM engine does not create a connection record when it processes a first data message of a data message flow in order to use this connection record in performing its address mapping for processing subsequent messages of the data message flow.

On the other hand, the NAT engine 1605 in some embodiments is a stateful element that performs its mapping by reference to a connection storage that stores connection records that reflect its prior SNAT mappings. When the NAT engine receives a data message, this engine in some embodiments first checks it connection storage to determine whether it previously created a connection record for the received message's flow. If so, the NAT engine uses the mapping contained in this record to perform its SNAT operation. Otherwise, it performs the SNAT operation based on a set of criteria that it uses to derive a new address mapping for the new data message flow. To do this, the NAT engine in some embodiments uses common network address translation techniques.

In some embodiments, the NAT engine can also use the connection storage in some embodiments when it receives a reply data message from the SaaS provider machine, in order to perform a DNAT operation to forward the reply data message to the tenant machine that sent the original message. In some embodiments, the connection record for each processed data message flow has a record identifier that includes the flow's identifier (e.g., five tuple identifier with the translated source network address).

In doing its mapping, the TM engines ensure that data message flows from different tenants that use the same source IP and port addresses are mapped to unique non-overlapping address spaces. For each message, the TM engine identifies the tenant ID and performs its address mapping based on this identifier. In some embodiments, the TM engine maps the source IP addresses of different tenants into different IP ranges such that any two messages from different tenants will not be mapped to the same IP address.

Consequently, each network type with a different tenant ID will map into a unique address within the full 2³²region of IP address (0.0.0.0-255.255.255.255). Classes A and B networks have 256 and 16 times more possible IP addresses than a class C network. Taking the size proportion of class A, B and C networks, 256 class A network could be allocated as the following: (1) 240 to map 240 tenants with class A network, (2) 15 to map 240 tenants with class B networks, and (3) a single class A network to map 240 tenants with class C networks. More specifically, in some embodiments, the lowest range class A networks (starting with 0.x.x.x/24, 1.x.x.x/24 . . . up to 239.x.x.x/24) will be used to map addresses coming from the 10.x class A network to 240 different target class A networks. The next 15 class A networks 240.x.x.x/24 to 254.x.x.x/24, each will be used to include each 16 class B networks (e.g., for a total of 240 networks (15*16)). The last class A network 255.x.x.x/24 will be used to include up to 256 private class C networks. Even though 256 tenants can be fitted, only 240 are used and 16 class C networks are not used. To summarize, some embodiments use the following mapping:

- 10.x.x.x/24 networks→1.x.x.x/24-239.x.x.x/24, resulting in 240 different mapping for each tenant;
- 172.16-31.x.x/12 networks→240.x.x.x/24-254.x.x.x/24, resulting in 240 different mapping for each tenant;
- 192.168.x.x/16→255.x.x.x/24 networks, resulting in 240 out of 256 possible mapping for each tenant.

The above-described schemes can support up to 240 tenants assuming that it is not known ahead of time what type of network class the tenants will use. In some embodiments, the public cloud network uses a private IP address. In such a case, it is desirable not to map into the private address space again. As some embodiments remove a class A network and a class B network, there are only 239 different tenants that can be supported in these embodiments. To achieve a unique mapping, some embodiments number all tenants ID from 1 to 239, and then add to the least significant 8 bits of the unmasked part of the private domain to the tenant ID (expressed in 8 bits) modulo 240. In this case, for class A addresses, the first tenant (number 1) will be mapped to 11.xx.xx.xx/24 and the last one (239) to 9.xx.xx.xx/24.

In the implementation illustrated in FIG. 16, some embodiments provide to each TM engine 1605 any potential tenant ID subnets and a way to route messages back to any specific IP address in each such subnet. This information can dynamically change when tenants, branches, and mobile devices are added or removed. Hence, this information has to be dynamically distributed to the TM engines in the Internet egress gateways of the virtual network. The amount of information distributed and regularly updated can be large as the egress Internet gateways of the virtual network provider might be used by a large number of tenants. Also, the restriction of 240 (or 239) of tenant's ID is a global one and can be solved only by adding multiple IPs addresses to the egress points.

FIG. 17 illustrates a double-NAT approach that is used in some embodiments instead of the single NAT approach illustrated in FIG. 16. The approach illustrated in FIG. 17 requires less tenant data to be distributed to most, if not all, TM engines and allows more private tenant networks to be mapped to the internal network of the virtual network provider. For a data message flow that traverses from a tenant machine through the virtual network 1700 and then the Internet 1625 to another machine (e.g., to a machine in a SaaS provider datacenter 1620), the approach illustrated in FIG. 17 places a NAT engine at the data message flow's ingress gateway 1770 into the virtual network and at this flow's egress gateway 1702 or 1704 out of the virtual network and into the Internet 1625. This approach also places the TM engines 1705 before the NAT engines 1712 of the ingress gateways 1770.

In the example illustrated in FIG. 17, the message flows come from two branch offices 1755 and 1760 and a datacenter 1765 of two virtual-network tenants, and enter the virtual network 1700 through the same ingress gateway 1770, although this does not necessarily have to be the case. Like the virtual network 1600, the virtual network 1700 in some embodiments is defined over multiple public cloud datacenters of multiple public cloud vendors. Also, in some embodiments, the virtual-network gateways 1702, 1704, and 1770 are part of the managed forwarding nodes, and the TM engines are placed in these embodiments before the NAT engines 215 in these MFNs.

The TM engines 1605 and 1705 operate similarly in FIGS. 16 and 17. Like TM engines 1605, the TM engine 1705 maps the source IP and port addresses of data messages entering the virtual network to new source IP and port addresses, when these data messages are destined to (i.e., have destination IP addresses for) SaaS provider datacenters 1620. For each such data message, the TM engine 1705 identifies the tenant ID and performs its address mapping based on this identifier.

Like the TM engines 1605, the TM engine 1705 in some embodiments is a stateless element and performs the mapping for each message through a static table without looking at any dynamic data structure. As a stateless element, the TM engine does not create a connection record when it processes a first data message of a data message flow in order to use this connection record in performing its address mapping for processing subsequent messages of the data message flow.

In doing its mapping, the TM engines 1705 in the ingress gateways 1770 ensure that data message flows from different tenants that use the same source IP and port addresses are mapped to unique non-overlapping address spaces. In some embodiments, the TM engine maps the source IP addresses of different tenants into different IP ranges such that any two messages from different tenants will not be mapped to the same IP address. In other embodiments, the TM engine 1705 might map the source IP addresses of two different tenants to the same source IP range, but different source port ranges. In still other embodiments, the TM engine maps two tenants to different source IP ranges, while mapping two other tenants to the same source IP range but different source port ranges.

Unlike the TM engines 1605, the TM engines 1705 at the virtual-network ingress gateways only need to identify tenants for branch offices, corporate datacenters, and corporate compute nodes that are connected to the ingress gateways. This significantly reduces the tenant data that needs to be initially supplied to, and periodically updated for, each TM engine. Also, as before, each TM engine can map only 239/240 tenants to unique address spaces. However, since the TM engines are placed at the ingress gateways of virtual network provider, the TM engines can each uniquely map 239/240 tenants.

The NAT engine 1712 of the ingress gateway 1770 in some embodiments can use either external public IP addresses or internal IP addresses that are specific to the public cloud (e.g. AWS, GCP or Azure) in which the ingress gateway 1770 resides. In either case, the NAT engine 1712 maps the source network address of an incoming message (i.e., a message entering the virtual network 1700) to an IP address that is unique within its ingress gateway's private cloud network. In some embodiments, the NAT engine 1712 translates the source IP address of each tenant's data message flows to a different unique IP address. In other embodiments, however, the NAT engine 1712 translates the source IP addresses of different tenants' data message flows to the same IP address, but uses the source port addresses to differentiate the data message flows of the different tenants. In still other embodiments, the NAT engine maps the source IP addresses of two tenants to different source IP ranges, while mapping the source IP addresses of two other tenants to the same source IP range but different source port ranges.

In some embodiments, the NAT engine 1712 is a stateful element that performs its mapping by reference to a connection storage that stores connection records that reflect its prior SNAT mappings. In some embodiments, the NAT engine can also use the connection storage in some embodiments when it receives a reply data message from the SaaS provider machine, in order to perform a DNAT operation to forward the reply data message to the tenant machine that sent the original message. The TM and NAT engines 1705, 1710 and 1712 are configured in some embodiments by the controller cluster 160 (e.g., are provided with tables for describing the mapping to use for different tenants and different ranges of network address space).

FIG. 18 presents an example that illustrates the source port translation of the ingress NAT engine 1712. Specifically, it shows the source address mapping that the tenancy mapping engine 1705 and the ingress NAT engine 1712 perform on a data message 1800 as it enters the virtual network 1700 through an ingress gateway 1770 and as it exits the virtual network at an egress gateway 1702. As shown, a tenant gateway 1810 sends the data message 1800, which arrives at the IPsec gateway 1805 with a source IP address of 10.1.1.13 and source port address of 4432. In some embodiments, these source addresses are addresses used by a tenant machine (not shown), while in other embodiments, one or both of these source addresses are source addresses that are produced by a source NAT operation performed by the tenant gateway or another network element in the tenant datacenter.

After this message has been processed by the IPsec gateway 1805, this gateway or another module of the ingress MFN associates this message with the tenant ID of 15, which identifies the virtual-network tenant to which the message 1800 belongs. Based on this tenant ID, the tenant mapping engine 1705 then maps the source IP and port addresses to source IP and port address pair of 15.1.1.13 and 253, as shown. This source IP and port addresses uniquely identify the message flow of the data message 1800. In some embodiments, the TM engine 1705 performs this mapping in a stateless manner (i.e., without reference to connection tracking records). In other embodiments, the TM engine performs this mapping in a stateful manner.

The ingress NAT engine 1712 next translates (1) the source IP address of the data message 1800 to a unique private or public (internal or external) IP address of 198.15.4.33, and (2) the source port address of this message to port address 714. In some embodiments, the virtual network uses this IP address for other data message flows of the same or different tenants. Hence, in these embodiments, the source network address translation (SNAT) operation of the NAT engine 1712 uses the source port addresses to differentiate different message flows of different tenants that use the same IP address within the virtual network.

In some embodiments, the source port address assigned by the ingress NAT engine's SNAT operation is also the source port address that is used to differentiate different message flows outside of the virtual network 1700. This is the case in the example illustrated in FIG. 18. As shown, the egress NAT engine 1710 in this example does not change the source port address of the data message when it performs its SNAT operation. Instead, it just changes the source IP address to an external IP address 198.15.7.125, which in some embodiments is the public IP address of the egress gateway(s) of the virtual network. This public IP address in some embodiments is also an IP address of the public cloud datacenter in which the ingress and egress gateways 1770 and 1702 operate.

With the source IP and port addresses 198.15.7.125 and 714, the data message is routed through the Internet to reach a gateway 1815 of a SaaS provider's datacenter. In this datacenter, a SaaS provider machine performs an operation based on this message and sends back a reply message 1900, the processing of which will be described below by reference to FIG. 19. In some embodiments, the SaaS provider machine performs one or more service operation (e.g., a middlebox service operation, such as firewall operation, IDS operation, IPS operation, etc.) on the data message, based on one or more service rules that are defined by reference to the source IP and port addresses 198.15.7.125 and 714. In some of these embodiments, different service rules for different tenants can specify the same source IP address (e.g., 198.15.7.125) in the rule identifiers while specifying different source port addresses in these rule identifiers. A rule identifier specifies a set of attributes for comparing to the data message flow attributes while performing a lookup operation that identifies a rule that matches a data message.

FIG. 19 illustrates the processing of a reply message 1900 that a SaaS machine (not shown) sends in response to its processing of the data message 1800. In some embodiments, the reply message 1900 can be identical to the original data message 1800, it can be a modified version of the original data message 1800, or it can be a completely new data message. As shown, the SaaS gateway 1815 sends the message 1900 based on the destination IP and port addresses 198.15.7.125 and 714, which are the source IP and port addresses of the data message 1800 when this message arrives at the SaaS gateway 1815.

The message 1900 is received at a gateway (not shown) of the virtual network, and this gateway provides the data message to the NAT engine 1710 that performed the last SNAT operation on the message 1800 before this message was sent to the SaaS provider. Although in the example illustrated in FIG. 19, the data message 1900 is received at the same NAT engine 1710 that performed the last SNAT operation, this does not have to be the case in each deployment.

The NAT engine 1710 (now acting as an ingress NAT engine) performs a DNAT (destination NAT) operation on the data message 1900. This operation changes the external destination IP address 198.15.7.125 to a destination IP address 198.15.4.33 that is used by the virtual network to forward the data message 1900 through the public cloud routing fabric and between the virtual network components. Again, the IP address 198.15.4.33 can be a public or private IP address in some embodiments.

As shown, the NAT engine 1712 (now acting as an egress NAT engine) receives the message 1900 after the NAT engine 1710 has translated its destination IP address. The NAT engine 1712 then performs a second DNAT operation on this message 1900, which replaces its destination IP and port addresses to 15.1.1.13 and 253. These addresses are the addresses recognized by the TM engine 1705. The TM engine 1705 replaces these addresses to the destination IP and port addresses of 10.1.1.13 and 4432, associates the data message 1900 with the tenant ID 15, and provides the message 1900 with this tenant ID to the IPsec gateway 1805 for forwarding to the tenant gateway 1810.

In some embodiments, a virtual network provider uses the above-described processes, systems, and components to provide multiple virtual WANs for multiple different tenants (e.g., multiple different corporate WANs for multiple corporations) over multiple public clouds of the same or different public cloud providers. FIG. 20 presents an example that shows M virtual corporate WANs 2015 for M tenants of a virtual network provider that has network infrastructure and controller cluster(s) 2010 in N public clouds 2005 of one or more public cloud providers.

Each tenant's virtual WAN 2015 can span all of the N public clouds 2005, or a subset of these public clouds. Each tenant's virtual WAN 2015 connects one or more branch offices 2020, datacenters 2025, SaaS provider datacenters 2030, and remote devices of the tenant. In some embodiments, each tenant's virtual WAN spans any public cloud 2005 that the VNP's controller cluster deems necessary for efficiently forwarding data messages between the different compute nodes 2020-2035 of the tenant. In selecting the public clouds, the controller cluster in some embodiments also accounts for public clouds that the tenant selects and/or the public clouds in which the tenant, or at least one SaaS provider of the tenant, has one or more machines.

The virtual WAN 2015 of each tenant allows the remote devices 2035 (e.g., mobile devices or remote computers) of the tenant to avoid interacting with the tenant's WAN gateway at any branch office or tenant datacenter, in order to access a SaaS provider service (i.e., to access a SaaS provider machine or machine cluster). The tenant's virtual WAN in some embodiments allows the remote devices to avoid the WAN gateways at the branch offices and tenant datacenters, by moving the functionalities of these WAN gateways (e.g., the WAN security gateways) to one or more machines in the public clouds spanned by the virtual WAN.

For example, to allow a remote device to access the compute resources of the tenant or its SaaS provider services, a WAN gateway in some embodiments has to enforce firewall rules that control how the remote device can access the tenant's computer resources or its SaaS provider services. To avoid branch or datacenter WAN gateways of the tenant, the tenant's firewall engines 210 are placed in the virtual network MFNs in one or more public clouds spanned by the tenant's virtual WAN.

The firewall engines 210 in these MFNs perform the firewall service operations on the data message flows from and to the remote devices. By performing these operations in the virtual network deployed over one or more public clouds, the data message traffic associated with the tenant's remote devices do not need to be unnecessarily routed through the tenant's datacenter(s) or branch offices in order to receive firewall rule processing. This alleviates traffic congestion in the tenant datacenters and branch offices, and avoids consuming expensive ingress/egress network bandwidth at these locations for processing traffic that is not destined to compute resources at these locations. It also helps speed up the forwarding of the data message traffic from and to the remote devices as this approach allows the intervening firewall rule processing to occur within the virtual network as the data message flows traverse to their destinations (e.g., at their ingress MFNs, egress MFNs or intermediate-hop MFNs).

In some embodiments, the firewall enforcing engine 210 (e.g., firewall service VM) of an MFN receives firewall rules form the VNP central controllers 160. A firewall rule in some embodiments includes a rule identifier and an action. The rule identifier in some embodiments includes one or more match values that are to be compared to data message attributes, such as layer 2 attributes (e.g., MAC addresses), layer 3 attributes (e.g., five tuple identifiers, etc.), tenant ID, location ID (e.g., office location ID, datacenter ID, remote user ID, etc.), in order to determine whether the firewall rule matches a data message.

The firewall rule's action in some embodiments specifies the action (e.g., allow, drop, re-direct, etc.) that the firewall enforcing engine 210 has to take on a data message when the firewall rule matches the data message's attributes. To address the possibility that multiple firewall rules match a data message, the firewall enforcing engine 210 stores the firewall rules (that it receives from the controller cluster 160) in a firewall rule data storage in a hierarchical manner so that one firewall rule can have higher priority than another firewall rule. When a data message matches two firewall rules, the firewall enforcing engine applies the rule with the higher priority in some embodiments. In other embodiments, the firewall enforcing engine examines the firewall rules according to their hierarchy (i.e., examines higher priority rules before lower priority rules) in order to ensure that it first matches the higher priority rule in case another lower priority rule might also be a match for the data message.

Some embodiments allow the controller cluster to configure the MFN components to have the firewall service engines examine a data message at an ingress node (e.g., node 850) as it enters a virtual network, at an intermediate node (e.g., node 857) on the virtual network or at an egress node (e.g., node 855) as it exits the virtual network. At each of these nodes, the CFE (e.g., 832, 856, or 858) in some embodiments calls its associated firewall service engine 210 to perform the firewall service operation on the data message that the CFE receives. In some embodiments, the firewall service engine returns its decision to the module that called it (e.g., to the CFE) so that this module can perform the firewall action on the data message, while in other embodiments, the firewall service engine performs its firewall action on the data message.

In some embodiments, other MFN components direct the firewall service engine to perform its operation. For instance, at an ingress node, the VPN gateway (e.g., 225 or 230) in some embodiments directs its associated firewall service engine to perform its operation, in order to determine whether the data message should be passed to the ingress node's CFE. Also, at the egress node, the CFE in some embodiments passes the data message to its associated firewall service engine, which if it decides to allow the data message through, then passes the data message through an external network (e.g., the Internet) to its destination, or passes the data message to its associated NAT engine 215 to perform its NAT operation before passing the data message to its destination through an external network.

The virtual network providers of some embodiments allow the tenant's WAN security gateway that is defined in the public clouds to implement other security services in addition to, or instead of, firewall services. For instance, a tenant's distributed WAN security gateway (which in some embodiments is distributed over each public cloud datacenter that is spanned by the tenant's virtual network) not only includes firewall service engines, but also includes intrusion detection engines and intrusion prevention engines. In some embodiments, the intrusion detection engines and intrusion prevention engines are incorporated architecturally in the MFN 150 to occupy similar position to the firewall service engine 210.

Each of these engines in some embodiments includes one or more storages that store intrusion detection/prevention policies distributed by the central controller cluster 160. In some embodiments, these policies configure the engines to detect/prevent unauthorized intrusions into the tenant's virtual network (that is deployed over several public cloud datacenters), and to take actions in response to detected intrusion events (e.g., generating logs, sending out notifications, shutting down services or machines, etc.). Like firewall rules, the intrusion detection/prevention policies can be enforced at various different managed forwarding nodes (e.g., ingress MFNs, intermediate MFNs, and/or egress MFNs of the data message flows) over which the virtual network is defined.

As mentioned above, the virtual network provider deploys each tenant's virtual WAN by deploying at least one MFN in each public cloud spanned by the virtual WAN, and configuring the deployed MFNs to define routes between the MFNs that allow the tenant's message flows to enter and exit the virtual WAN. Also, as mentioned above, each MFN can be shared by different tenants in some embodiments, while in other embodiments each MFN is deployed for just one particular tenant.

In some embodiments, each tenant's virtual WAN is a secure virtual WAN that is established by connecting the MFNs used by that WAN through overlay tunnels. This overlay tunnel approach in some embodiments encapsulates each tenant's data message flows with a tunnel header that is unique to each tenant, e.g., contains a tenant identifier that uniquely identifies the tenant. For a tenant, the virtual network provider's CFEs in some embodiments use one tunnel header to identify ingress/egress forwarding elements for entering/exiting the tenant's virtual WAN, and another tunnel header to traverse intervening forwarding elements of the virtual network. The virtual WAN's CFEs use different overlay encapsulation mechanisms in other embodiments.

To deploy a virtual WAN for a tenant over one or more public clouds, the VNP's controller cluster (1) identifies possible edge MFNs (that can serve as ingress or egress MFNs for different data message flows) for the tenant based on locations of the tenant's corporate compute nodes (e.g., branch offices, datacenters, mobile users, and SaaS providers), and (2) identifies routes between all possible edge MFNs. Once these routes are identified they are propagated to the forwarding tables of the CFEs (e.g., propagated using OpenFlow to different OVS-based virtual network routers). Specifically, to identify optimal routes through a tenant's virtual WAN, the MFNs associated with this WAN generate measurement values that quantify the quality of the network connection between them and their neighboring MFNs, and regularly provide their measurements to the VNP's controller cluster.

As mentioned above, the controller cluster then aggregates the measurements from the different MFNs, generates routing graphs based on these measurements, defines routes through a tenant's virtual WAN, and then distributes these routes to the forwarding elements of the CFEs of the MFNs. To dynamically update the defined routes for a tenant's virtual WAN, the MFNs associated with this WAN periodically generate their measurements and provide these measurements to the controller cluster, which then periodically repeats its measurement aggregation, route-graph generation, route identification, and route distribution based on the updated measurements that it receives.

In defining the routes through a tenant's virtual WAN, the VNP's controller cluster optimizes the routes for the desired end-to-end performance, reliability and security, while trying to minimize the routing of tenant's message flows through the Internet. The controller cluster also configures the MFN components to optimize the layer 4 processing of the data message flows passing through the network (e.g., to optimize the end-to-end rate of TCP connections by splitting the rate control mechanisms across the connection path).

With the proliferation of public clouds, it is often very easy to find a major public cloud datacenter close to each branch office of a corporation. Similarly, SaaS vendors are increasingly hosting their applications within public clouds, or are similarly located at the vicinity of some public cloud datacenter. Consequently, the virtual corporate WANs 2015 securely use the public clouds 2005 as corporate network infrastructure that have presence in the vicinity of the corporate compute nodes (e.g., branch offices, datacenters, remote devices, and SaaS providers).

Corporate WANs require bandwidth guarantees in order to provide business critical application at an acceptable performance at all times. Such applications maybe interactive data applications, e.g. ERP, financial or procurement, deadline-oriented application (e.g., industrial or IoT control), real time application (e.g., VoIP or video conferencing). Consequently, traditional WAN infrastructure (e.g., Frame Relay or MPLS) provides such guarantees.

A main obstacle in providing bandwidth guarantee in a multi-tenant network is the need to reserve bandwidth over one or more path for a certain customer. In some embodiments, the VNP offers QoS services and provides an Ingress Committed Rate (ICR) guarantee and an Egress Committed Rate (ECR) guarantee. ICR refers to the traffic rate coming into the virtual network, while ECR refers to the traffic rate exiting the virtual network to the tenant site.

As long as traffic does not exceed ICR and ECR limits, the virtual network in some embodiments provides bandwidth and delay guarantees. For example, as long as HTTP ingress or egress traffic do not exceed 1 Mbps, the bandwidth and low delay are guaranteed. This is the point-to-cloud model because, for QoS purposes, the VNP need not keep track of traffic destinations, as long as its destinations are within the ICR/ECR bounds. This model is sometimes called the hose model.

For the more stringent applications, where a customer desires a point-to-point guarantee, a virtual data pipe needs to be constructed to deliver the highly critical traffic. For example, an enterprise may want two hub sites or datacenters connected with high service level agreement guarantees. To that end, VNP routing automatically chooses a routing path that satisfies the bandwidth constraint for each customer. This is referred to as the point-to-point model or the pipe model.

The main advantage of VNP in providing guaranteed bandwidth to end users is the ability to adjust the VNP infrastructure according to the changing bandwidth demands. Most public clouds provide minimum bandwidth guarantees between each two instances located at different regions of the same cloud. If the current network does not have enough unused capacity to provide the guaranteed bandwidth for a new request, the VNP adds new resources to its facilities. For example, the VNP can add new CFEs in high-demand regions.

One challenge is to optimize the performance and the cost of this new dimension in planning routes and scaling up and down the infrastructure. To facilitate the algorithms and bandwidth accounting, some embodiments assume that end-to-end bandwidth reservations are not split. In other ways, if a certain bandwidth (e.g., 10 Mbps) is reserved between branch A and branch B of a certain tenant, the bandwidth is allocated over a single path that starts from an ingress CFE to which branch A connects, and then traverses a set of zero or more intermediate CFEs to reach the egress CFE that is connected to branch B. Some embodiments also assume that the bandwidth guaranteed path only traverse a single public cloud.

In order to account for the various bandwidth reservation that intersect over the network topology, the VNP in some embodiments defines the routing over a reserved bandwidth path statically, so that data message flows always traverse through the same routes that were reserved for the bandwidth requirements. In some embodiments, each route is identified with a single tag that each CFE traversed by the route matches to a single outgoing interface associated with this route. Specifically, each CFE matches a single outgoing interface to each data message that has this tag in its header and arrives from a specific incoming interface.

In some embodiments, the controller cluster maintains a network graph that is formed by several interconnected nodes. Each node n in the graph has the allocated total guaranteed bandwidth (TBW_n) associated with this node and the amount of bandwidth already reserved (allocated to a certain reserved path) by this node (RBW_n). In addition, for each node, the graph includes the cost in cents per gigabyte (C_ij) and the delay in milliseconds (D_ij) associated with sending traffic between this node and all other nodes in the graph. The weight associated with sending traffic between node i and node j is W_ij=a*C_ij+D_ij, where a is a system parameter that is typically between 1 and 10.

When a request for a bandwidth reservation of value BW between branches A and B is accepted, the controller cluster first maps the request to specific ingress and egress routers n and m, which are bound to branches A and B respectively. The controller cluster then executes a routing process that conducts two lowest-cost (e.g., shortest path) computations between n and m. The first is a lowest-cost (e.g., shortest path) route between n and m irrespective of the available bandwidth along the computed route. The total weight of this route is computed as Wi.

The second lowest-cost (e.g., shortest path) computation initially modifies the graph by eliminating all nodes i where BW>TBW_i−RBW_i. The modified graph is termed the trimmed graph. The controller cluster then performs a second lowest-cost (e.g., shortest path) route computation over the trimmed graph. If the weight of the second route is no more than K percent (K is typically 10%-30%) higher than the first route, the second route is selected as the preferred path. On the other hand, when this requirement is not met, the controller cluster will add to the first path the node i with the smallest value of TBW_i−RBW_i, and then repeats the two lowest-cost (e.g., shortest path) computations. The controller cluster will continue adding more routers until the condition is met. At that point, the reserved bandwidth BW is added to all RBW_iwhere i is a router on the selected route.

For the special case of a request for additional bandwidth for a route that already has reserved bandwidth, the controller cluster will first delete the current bandwidth reservation between nodes A and B and will calculate the path for the total bandwidth request between these nodes. To do this, the information held for each node in some embodiments also includes the bandwidth reserved for each tag, or each source and destination branches, and not only the overall bandwidth reserved. After bandwidth reservations are added to the network, some embodiments do not revisit the routes so long as there are no major changes in measured network delays or costs through the virtual network. However, when the measurements and/or costs change, these embodiments repeat the bandwidth reservation and route computation processes.

FIG. 21 conceptually illustrates a process 2100 performed by the controller cluster 160 of the virtual network provider to deploy and manage a virtual WAN for a particular tenant. In some embodiments, the process 2100 is performed by several different controller programs executing on the controller cluster 160. The operations of this process do not necessarily have to follow the sequence illustrated in FIG. 21, as these operations can be performed by the different programs in parallel or in a different sequence. Accordingly, these operations are illustrated in this figure only to describe one exemplary sequence of operations performed by the controller cluster.

As shown, the controller cluster initially deploys (at 2105) several MFNs in several public cloud datacenters of several different public cloud providers (e.g., Amazon AWS, Google GCP, etc.). The controller cluster in some embodiments configures (at 2105) these deployed MFNs for one or more other tenants that are different than the particular tenant for which the process 2100 is illustrated.

At 2110, the controller cluster receives from the particular tenant data about external machine attributes and locations of the particular tenant. In some embodiments, this data includes the private subnets used by the particular tenant as well as identifiers for one or more tenant offices and datacenters at which the particular tenant has external machines. In some embodiments, the controller cluster can receive the tenant data through APIs or through a user interface that the controller cluster provides.

Next, at 2115, the controller cluster generates a routing graph for the particular tenant from the measurements collected by the measurement agents 205 of the MFNs 150 that are candidate MFNs to use for establishing the virtual network for the particular tenant. As mentioned above, the routing graph has nodes that represent the MFNs, and links between the nodes that represent the network connections between the MFNs. The links have associated weights, which are cost values that quantify the quality and/or cost of using the network connections represented by the links. As mentioned above, the controller cluster first generates a measurement graph from the collected measurements, and then generates the routing graph by removing links from the measurement graph that are not optimal (e.g., that have large delays or drop rates).

After constructing the routing graph, the controller cluster performs (at 2120) path searches to identify possible routes between different pairs of candidate ingress and egress nodes (i.e., MFNs) that the tenant's external machines can use to send data messages into the virtual network (deployed by the MFNs) and to receive data messages from the virtual network. In some embodiments, the controller cluster uses known path search algorithms to identify different paths between each candidate ingress/egress pair of nodes. Each path for such a pair uses one or more links that when concatenated traverse from the ingress node to the egress node through zero or more intermediate nodes.

In some embodiments, the cost between any two MFNs comprises a weighted sum of estimated latency and financial costs for a connection link between the two MFNs. The latency and financial costs include in some embodiments one or more of the following: (1) link delay measurements, (2) estimated message processing latency, (3) cloud charges for outgoing traffic from a particular datacenter either to another datacenter of the same public cloud provider, or to exit the public cloud (PC) provider's cloud (e.g., to another public cloud datacenter of another public cloud provider or to the Internet), and (4) estimated message processing costs associated with the MFNs executing on host computers in the public clouds.

Some embodiments assess a penalty for connection links between two MFNs that traverse through the public Internet, in order to minimize such traversal whenever possible. Some embodiments also incentivize the use of private network connections between two datacenters (e.g., by reducing the connection link cost) in order to bias the route generation towards using such connections. Using the computed costs of these pair-wise links, the controller cluster can compute the cost of each routing path that uses one or more of these pair-wise links by aggregating the costs of the individual pair-wise links that are used by the routing path.

The controller cluster then selects (at 2120) one or up to N identified paths (where N is an integer larger than 1) based on the computed costs (e.g., the lowest aggregate cost) of the identified candidate paths between each candidate ingress/egress pair of nodes. In some embodiments, the computed costs for each path are based on the weight cost of each link used by the path (e.g., is a sum of each link's associated weight value), as mentioned above. The controller cluster can select more than one path between a pair of ingress/egress nodes when more than one route is needed between two MFNs to allow the ingress MFN or an intermediate MFN to perform a multi-path operation.

After selecting (at 2120) one or N paths for each candidate pair of ingress/egress nodes, the controller cluster defines one or N routes based on the selected paths, and then generates route tables or route table portions for the MFNs that implement the particular tenant's virtual network. The generated route records identify edge MFNs to reach different subnets of the particular tenant, and identify next hop MFNs for traversing routes from ingress MFNs to egress MFNs.

At 2125, the controller cluster distributes route records to the MFNs in order to configure the forwarding elements 235 of these MFNs to implement the virtual network for the particular tenant. In some embodiments, the controller cluster communicates with the forwarding elements to pass the route records by using communication protocols that are presently used in a software defined multi-tenant datacenter to configure software routers executing on host computers to implement a logical network that spans the host computers.

Once the MFNs have been configured and the virtual network is operational for the particular tenant, the edge MFNs receive data messages from tenant's external machines (i.e., machines outside of the virtual network) and forward these data messages to edge MFNs in the virtual network, which in turn forward the data messages to other external machines of the tenant. While performing such forwarding operations, the ingress, intermediate and egress MFNs collect statistics regarding their forwarding operations. Also, in some embodiments, one or more modules on each MFN in some embodiments collect other statistics regarding network or compute consumption in the public cloud datacenters. In some embodiments, the public cloud providers collect such consumption data and pass the collected data to the virtual network provider.

When approaching a billing cycle, the controller cluster collects (e.g., at 2130) statistics collected by the MFNs, and/or the network/compute consumption data collected by the MFNs or provided by the public cloud providers. Based on the collected statistics, and/or provided the network/compute consumption data, the controller cluster generates (at 2130) billing reports and sends the billing reports to the particular tenant.

As mentioned above, the amount billed in the billing report accounts for statistics and network/consumption data that the controller cluster receives (e.g., at 2130). Also, in some embodiments, the bill accounts for the cost that the virtual network provider incurred to operate the MFNs (that implement the virtual network for the particular tenant) plus a rate of return (e.g., a 10% increase). This billing scheme is convenient for the particular tenant because the particular tenant does not have to deal with bills from multiple different public cloud providers over which the tenant's virtual network is deployed. The VNP's incurred cost in some embodiments includes the cost charged to the VNP by the public cloud providers. At 2130, the controller cluster also charges a credit card or electronically withdraws funds from a bank account for the charges reflected in the billing report.

At 2135, the controller cluster determines whether it has received new measurements from the measurement agents 205. If not, the process transitions to 2145, which will be described below. On the other hand, when the controller cluster determines that it has received new measurements from the measurement agents, it determines (at 2140) whether it needs to re-examine its routing graph for the particular tenant based on the new measurements. Absent an MFN failure, the controller cluster in some embodiments at most updates its routing graph for each tenant once during a particular time period (e.g., once every 24 hours or every week) based on received, updated measurements.

When the controller cluster determines (at 2140) that it needs to re-examine the routing graph based on new measurements that it has received, the process generates (at 2145) a new measurement graph based on the newly received measurements. In some embodiments, the controller cluster uses a weighted sum to blend each new measurement with the prior measurements in order to ensure that the measurement values associated with the links of the measurement graph do not fluctuate dramatically each time a new measurement set is received.

At 2145, the controller cluster also determines whether it needs to adjust the routing graph based on the adjusted measurement graph (e.g., whether it needs to adjust weight values for the routing-graph links, or add or remove links in the routing graph because of adjusted measurement values associated with the links). If so, the controller cluster (at 2145) adjusts the routing graph, performs path search operations (such as operations 2120) to identify routes between ingress/egress node pairs, generates route records based on the identified routes, and distributes route records to the MFNs. From 2145, the process transitions to 2150.

The process also transitions to 2150 when the controller cluster determines (at 2140) that it does not need to re-examine the routing graph. At 2150, the controller cluster determines whether it has to collect statistics regarding data messages processed and network/compute resources consumed. If not, the process returns to 2135 to determine whether it has received new measurements from the MFN measurement agents. Otherwise, the process returns to 2130 to collect statistics and network/compute consumption data, and to generate and send billing reports. In some embodiments, the controller cluster repeatedly performs the operations of the process 2100 until the particular tenant no longer needs a virtual network that is deployed across the public cloud datacenters.

In some embodiments, the controller cluster not only deploys virtual networks for tenants in the public cloud datacenters, but also assists the tenants in deploying and configuring compute node machines and service machines in the public cloud datacenters. The deployed service machines can be machines separate from the service machines of the MFNs. In some embodiments, the controller cluster billing report to the particular tenant also accounts for compute resources consumed by the deployed compute and service machines. Again, having one bill from one virtual network provider for network and compute resources consumed in multiple public cloud datacenters of multiple public cloud providers is more preferable for the tenant than receiving multiple bills from multiple public cloud providers.

Other embodiments use other deployment models to deploy single or multi-tenant virtual networks over the network and compute infrastructure of two or more public cloud providers. For instance, in some embodiments, the virtual network provider allows one or more cloud service resellers to deploy single or multi-tenant virtual networks for one or more of their customers. FIG. 22 illustrates this deployment model. As shown, this deployment model uses three levels of SaaS providers 2205-2215 that provide three sets of SaaS services.

The first SaaS layer 2205 is provided by one or more public cloud providers 2220 that provide compute and network infrastructure (e.g., compute elements (such as host computers, VMs and/or containers) and network elements (hardware or software switches, routers, middlebox elements, etc.) that connect the compute elements) in multiple different public clouds 2250. The second SaaS layer 2210 is provided by the VNP 2225, which provides the tools for deploying virtual networks across multiple public clouds 2250 for a cloud reseller. The cloud reseller 2230 provides the third SaaS layer 2215 to its customers 2240, which use the cloud resellers tools to define compute elements and network infrastructure (e.g., virtual network) to deploy across one or more public clouds 2250.

The cloud reseller 2230 in some embodiments has its own customer account with each of the public cloud providers. In some embodiments, the cloud reseller establishes its customer account with each public cloud provider directly (as identified by dashed arc 2252) and then provides security credentials for this customer account to the VNP provider 2225. In other embodiments, the cloud reseller establishes its public cloud customer account through the VNP provider's machines 2225. To direct the VNP to deploy a virtual network for one of its tenants over public cloud infrastructure, the cloud reseller machines 2230 initially provide the cloud reseller's VNP security credentials (e.g., username, password, certificate, etc.) to the VNP machines 2225 in order to authenticate the cloud reseller.

As shown, the cloud reseller also provides the desired network configuration to the VNP 2225. This configuration described the attributes of the virtual network that needs to be deployed for a tenant. In some embodiments, this configuration data also includes a tenant identifier that the cloud reseller 2230 uses for its customer for which it directs the VNP 2225 to deploy a virtual network. This tenant identifier in some embodiments is obtained from the VNP. In other embodiments, the configuration data includes a tenant identifier provided by the VNP, which the cloud reseller maps to its own tenant identifier for its tenant.

In some embodiments, the VNP machines 2225 provide the cloud reseller's security credentials to the public cloud machines 2220 when deploying virtual networks (e.g., when deploying MFNs, configuring MFN CFEs with proper routing records, etc.) for the reseller's customers. These security credentials (e.g., user name, password, security certificate, etc.) allow the VNP machines to authenticate themselves as machines operating at the behest of the cloud reseller (e.g., to logon onto the public cloud machines 2220 as if the cloud reseller machines 2230 are logging on to the public cloud machines 2220).

To deploy the virtual network for each tenant of the cloud reseller, the VNP machines also configure in some embodiments the components that form the virtual network (e.g., the MFN, MFN gateways/service boxes/CFEs, etc.) with the tenant identifier for the cloud reseller's tenant. In some embodiments, these configured components then associate the statistics that they collect (e.g., the routing statistics and deployment machine compute statistics) with the tenant identifiers, so that the cloud reseller's customers can be appropriately charged based on these statistics, as further described below.

In some of the embodiments in which the cloud reseller provides the tenant identifiers for its customers to the VNP machines 2225, the VNP machines 2225 map each tenant identifier provided by the cloud reseller 2230 to a unique tenant identifier of the VNP, and translate the VNP tenant identifiers for the collected statistics to the cloud reseller's tenant identifiers before providing the statistics to the cloud reseller. In other embodiments, the cloud reseller machines 2230 and/or VNP provider machines 2225 use tenant identifiers provided by the public cloud provider machines 2220.

The VNP machines 2225 uses the tenant network configuration data provided by the cloud reseller 2230 to deploy and/or configure the virtual network components (e.g., the MFNs, etc.) of the virtual network for each customer of the cloud reseller. In some embodiments, multiple different customers share the same virtual network and/or virtual network components. Conjunctively, or alternatively, the VNP machines 2225 in some embodiments can define single tenant virtual networks for any individual customer of the cloud reseller. As shown, the VNP machines 2225 deploy and configure the virtual networks and/or virtual network components through the public cloud provider machines 2220. In some embodiments, reseller, VNP, and public cloud machines 2220-2230 communicate with each other through their respective API interfaces and intervening network fabric (e.g., the Internet).

The VNP machines 2225 collect per tenant statistics from the deployment virtual network components (e.g., routing statistics from the CFEs and gateways, etc.), aggregate the statistics, and forward the aggregated statistics to the cloud reseller. In the embodiments in which the VNP machines map the reseller customer identifiers to VNP tenant identifiers, the VNP machines translate the tenant identifiers to the customer identifiers before supplying the aggregated statistics with the translated customer identifiers. Also, in the embodiments in which the VNP machines map the public cloud identifiers to VNP tenant identifiers, the VNP machines translate the public cloud identifiers to the VNP identifiers.

As shown, the VNP machines 2225 periodically send billing reports to the cloud reseller 2230 to collect fees for services performed by the VNP. In some embodiments, these billing reports charge the cloud reseller for the VNP's service fees for deploying virtual networks for the cloud reseller over two or more public clouds. These deployment charges include fees for performing ancillary operations in support of such deployments, such as measurement operations that produce measurements that quantify the quality and/or cost of links between MFNs in the public clouds and between external machine locations of the tenants.

Also, the VNP machines 2225 in some embodiments receive billing data from one or more public cloud providers for the cloud reseller. This billing data is associated with the cloud reseller's customer credentials (e.g., PC provider customer number for the cloud reseller) in some embodiments. The VNP machines 2225 in these embodiments pass along the billing data to the cloud reseller (e.g., with a markup adjustment or without a markup adjustment). In other embodiments, the public cloud providers send billing reports directly to the cloud reseller machines 2230, as shown by dashed lines 2252.

The cloud reseller machines 2230 in some embodiments uses the usage statistics provided by the VNP machines 2225 to charge its customers for the virtual networks. In some embodiments, the VNP machines 2225 not only deploy network infrastructure but also deploy compute infrastructure for the cloud reseller 2230. In some of these embodiments, the usage statistics reflects the used compute resources and the cloud reseller machines 2230 use these statistics to charge the reseller's customers. In some embodiments, the cloud reseller does not use the collected statistics to charge its customers, but rather charges its customers based on the compute and/or network configuration that the customer requested for deployment.

To further illustrate the differences between the three-layer SaaS deployment model of FIG. 22, FIG. 23 illustrates a similar diagram for the two-layer SaaS deployment model that was previously described above. This two-layer model of FIG. 23 does not have any cloud reseller 2215. Rather, in this two-layer model, the VNP's customers are entities that have the VNP deploy a single-tenant virtual network just for them, or to share a multi-tenant virtual network that the VNP has deployed for several customers. As described above, different customers can securely share the components the define the multi-tenant virtual network over one or more public clouds, as each customers network traffic is securely segregated from the network traffic of other customers.

In the two-layer model of FIG. 23, the first SaaS layer 2205 is provided by one or more public cloud providers 2220 that provide compute and network infrastructure in multiple different public clouds 2250, while the second SaaS layer 2210 is provided by the VNP 2225, which provides the tools for deploying virtual networks across multiple public clouds 2250 for several of its customers. As shown, the VNP machines 2225 provide to the public cloud providers the VNP security credentials for the public clouds.

The VNP machines receive for each customer (associated with a tenant identifier) tenant network configuration data, and based on this data, deploy and/or configure the virtual network components (e.g., the MFNs, etc.) of the virtual network for each of its customers. As shown, the VNP machines 2225 deploy and configure the virtual networks and/or virtual network components through the public cloud provider machines 2220. In some embodiments, VNP and and public cloud machines 2220 and 2225 communicate with each other through their respective API interfaces and intervening network fabric (e.g., the Internet).

As further shown, the VNP machines 2225 collect per tenant statistics from the deployment virtual network components (e.g., routing statistics from the CFEs and gateways, etc.), and aggregate the collected statistics. The VNP machines 2225 periodically send billing reports to each of the VNP customers to collect fees for services performed by the VNP. As mentioned above, these fees in some embodiments include the fees that the public cloud providers charged the VNP for the resources (e.g., compute and/or network resources) consumed by the customer's virtual network, plus a certain markup percentage. The VNP machines identify the amount of resources by each customer's virtual network based on the statistics that these machines collect and aggregate for the customer's associated identifier. In other embodiments, the VNP machines pass through to each customer each public cloud provider's charge for the resources consumed by the customer's virtual network, plus a charge for each customer's use of VNP resources.

Some embodiments connect a multi-machine compute node (e.g., a branch office or datacenter) of a tenant to the tenant's public cloud virtual network through multiple connection links to multiple public clouds (e.g., multiple public cloud datacenters) of one or more public cloud providers. Having multiple links between the multi-machine compute node (MMCN) and the public cloud virtual network allows the virtual network to be highly available, as it can withstand the failure of one or more connection links. In some embodiments, one link is a primary link while each other link is a failover link. This approach also allows the best route to be established from each MMCN to each other MMCN or SaaS provider datacenter by selecting the best ingress node for entering the virtual network from the MMCN for the best routing path through the virtual network to the egress node for exiting the virtual network to another MMCN or SaaS provider datacenter.

The discussion below uses the term multi-homed MMCN to refer to a multi-machine compute node of a tenant that connects to the tenant's public cloud virtual network through multiple connection links to multiple public clouds of one or more public cloud providers. The discussion below also uses the term multi-homed SaaS datacenter to refer to a SaaS datacenter to which a virtual network associates multiple MFNs in one or more public clouds (e.g., multiple public cloud datacenters) of one or more public cloud providers. These MFNs in some embodiments are candidate egress nodes for routes that traverse through the virtual network to reach a SaaS provider. The use of two or more egress nodes to connect the virtual network of a SaaS datacenter is also advantageous in that it enables link failover support and it allows for the use of optimal routes between different pairs of external computer node (e.g., remote computer or MMCN) and SaaS provider datacenter.

In some embodiments, a SaaS datacenter does not need to initiate routes to multiple MFNs of the virtual network in multiple public cloud datacenters, even when the virtual network controllers 160 associate multiple MFNs with the SaaS datacenter. On the other hand, multi-homed MMCNs in some embodiments need to actively initiate routes through different links to the virtual network. To do this, providing a fallback capability is facilitated in a multi-homed MMCN by having an appropriate router with failover capabilities (e.g., by using a Cisco 2800 series).

For optimal routing, the multi-homed MMCN includes in some embodiments one or more computers or appliances that execute measurement processes to measure the performance (delay, loss etc.) between the MMCN and the different public cloud datacenters to which the MMCN can connect. In addition, the MMCN in some embodiments performs its overall routing operations based on routes that are defined by the centralized controller cluster (e.g., controller cluster 160) that defines the virtual network for an entity (e.g., for a tenant). To accomplish this, the multi-homed MMCN is equipped with SD-WAN capabilities (such as Velocloud and Viptela appliances) that operate as a part of the centralized control plane for deploying the virtual network. As mentioned above, the centralized control plane is implemented by a cluster of two or more controllers in some embodiments.

FIG. 24 illustrates a process 2400 used by the central controller cluster of some embodiments to define routes for a particular multi-homed MMCN. This process uses a specialized router in the particular multi-homed MMCN to use the defined routes to perform routing operations that forward data messages from the MMCN to the virtual network through the multiple connection links. The specialized router is a software router or a cluster of software routers in some embodiment, while it is a routing appliance (e.g., SD-WAN appliance) in other embodiments. The specialized router or router cluster in the MMCN is referred to as the edge node of the MMCN in the discussion below. In some embodiments, the central controller cluster remotely controls the edge nodes of the MMCNs through the Internet.

As shown, the central controller cluster initially identifies (at 2405) a subset of N MFNs (e.g., 10 to 12) from N different cloud regions that are the closest to the particular MMCN edge node according to a DNS server service and the edge node IP address. The N MFNs in some embodiments have at least one candidate MFN from each cloud provider within a certain distance of the particular MMCN. Also, in some embodiments, each of the N MFNs includes a gateway (e.g., a branch gateway 225) to establish a secure connection link with the particular MMCN edge node.

Also, as mentioned above, the DNS server service in some embodiments is a service machine, or a cluster of several service machines, that operates in one or more public clouds and that provides DNS information to DNS servers of the MMCNs of an entity. In some of these embodiments, the DNS servers are operated by the virtual network provider or by another entity. In other embodiments, the DNS server service is a geo-IP service (e.g., of a third party) that resides outside of the public clouds that implement the virtual network and that can identify edge nodes in the public clouds that are near the particular MMCN for which the process 2400 is performed.

Next, at 2410, the controller cluster downloads the identified list to N nodes to the particular MMCN's edge node so that the MMCN's edge node can take measurements that quantify the quality of connections to each of the N MFNs in the list. In some embodiments, each MMCN edge node has a measurement agent (e.g., a process executing on one of the MMCN computers) that generates such measurements. This measurement agent generates measurement values differently in different embodiments. In some embodiments, the measurement agent sends pinging messages (e.g., UDP echo messages) periodically (e.g., once every second, every N seconds, every minute, every M minutes, etc.) to each of the measurement agents of the N MFNs in the list. Based on the speed of the reply messages that it receives, the MMCN measurement agent computes and updates measurement metric values, such as network-connection throughput speed, delay, loss, and link reliability. In some embodiments, multiple MFNs share one measurement agent (e.g., in the same datacenter or nearby datacenter of the public cloud provider hosting the MFNs).

In some embodiments, the particular MMCN's measurement agent periodically performs these measurements, and periodically sends the new measurements to the controller cluster so that the controller cluster can update its weight computations and route generations, as further described below by reference to 2420-2435. Also, whenever new MFNs are added in newly added or previously used public cloud datacenters, the controller cluster in some embodiments generates update lists of N candidate MFNs.

At 2415, the controller cluster receives the measurements taken by the particular MMCN's edge node. Based on these measurements, the centralized controller cluster computes (at 2420) a link weight for each connection link that connects the particular MMCN's edge node to each of the N MFNs. For instance, in some embodiments, the central controller computes each link's weight by using an exponential filter on the delay measurements and using the loss parameter as weight multiplier (e.g., doubling the weight for each 1% of loss).

Based on the computed weights, the central controller then identifies (at 2425) a subset of the M (e.g., 5 or 6) MFNs as the “home” nodes to which the edge node will be connected. In some embodiments, the M nodes are the nodes with the lowest weight values. In other embodiments, the M nodes are the nodes with the lowest weight values, but at least one representative MFN in each cloud provider is included in the M nodes. The list of M nodes may change with time and MFNs can be dropped and added to the list as new MFNs are added and/or as new measurements are received from the particular MMCN edge node. The controller cluster in some embodiments uses a “hysteresis” process to avoid frequent changes in the list of M MFNs. The hysteresis process in some embodiments uses previous state (i.e., previous members) of the MFN list to reduce the rate of adding/removing members to/from the MFN list. Also, in some embodiments, the controller cluster will not drop an MFN from the list unless another MFN has a 10% smaller average weight for a window (e.g., a time period) of measurements.

As mentioned above, the particular MMCN edge node in some embodiments maintains a secure connection (e.g., an IPsec connection) to the virtual network gateway of each of the M MFNs. In some embodiments, the controller cluster directs (at 2425) the particular MMCN edge node to establish secure connections with each of the M MFNs. At 2430, the controller cluster uses the computed weights of the selected M MFNs to identify optimal routes and failover routes for connecting the particular MMCN edge node with each other possible nodes for data message flows to traverse between the particular MMCN edge node and other MMCN edge nodes or SaaS provider datacenters through the virtual network. To generate such routes, the controller cluster in some embodiments uses shortest path route-identification processes, as described above.

In some embodiments, the controller cluster repeats its route identification process periodically, or whenever the computed weight values change (e.g., based on new measurements or addition/deletion of MFNs in the list of M MFNs). In some embodiments, the controller cluster performs the route identification operation (at 2430) for the particular MMCN edge node along with the route identification operation for other multi-homed MMCNs and/or multi-homed SaaS providers together, as multiple connection links to other MMCNs and to the SaaS providers would be relevant in identifying optimal routes to and from the particular MMCN. These computed routes also account for routes to/from virtual network MFNs that are candidates for connecting to remote devices (e.g., remote laptops, desktops, or mobile devices, such as smartphones, tablets, etc.).

After identifying these routes, the controller cluster supplies (at 2435) forwarding records for one or more routes to the particular MMCN edge node and the MFNs. For instance, in some embodiments, the controller cluster provides forwarding records (e.g., routing records that specify the next hop, routing records that specify the virtual network egress node, etc.) to particular MMCN edge node and to the MFN CFEs. By using these forwarding records to perform their routing operations, the particular MMCN edge node and MFN CFEs implement the optimal and failover routes defined (at 2430) by the controller cluster. In some embodiments, the controller cluster supplies new routing records to the particular MMCN edge node and the MFN CFEs whenever it identifies new optimal or failover routes.

In this manner, the process 2400 of FIG. 24 bases its routing computations on computed weight values that express the quality of the connection between the particular MMCN and each of its several connections to the virtual network. Under this approach, a different virtual-network ingress/egress node pair can be selected for the particular MMCN edge node and different MMCNs, different SaaS nodes and/or different remote devices. Because of this, the controller cluster in some embodiments performs the route identification operation (i.e., operation 2430) for one or more multi-homed MMCNs and/or multi-homed SaaS providers together, as mentioned above.

FIG. 25 presents an example of two branch nodes EN1 and EN2 of two MMCNs 2505 and 2510 and a SaaS datacenter 2515. Each of the branch nodes connects to the virtual network 2520 through virtual-network MFNs 2525-2535 that are defined in three public cloud datacenters of two or three public cloud providers. The SaaS datacenter 2515, on the other hand, can be accesses through virtual-network MFNs 2540 and 2545. The weight measured between the relevant branch nodes EN1 and EN2, MFNs 2525-2545 and SaaS datacenter 2515 are depicted on the links between these nodes. In this example, it is assumed that other weights, like between nodes 2525 and 2535 are much higher (e.g. 10), so that no shortest path routing algorithm will use them in the best cost path.

As can be seen from this example, the best path from EN1 to the SaaS datacenter traverses nodes 2525 and 2540 as this path has a weight sum of 14, which is smaller than other weight costs of other paths. For instance, going through node 2530 will incur a smaller weight in the first hop but will result in a total minimal weight of 15. The optimal route from branch node EN2 will be through router 2535 and 2545 with a total weight of 15. Consequently, the two branches will use two different routes to reach the SaaS datacenter. To communicate between EN1 and EN2, the best route will be through MFN 2530 with a total weight of 13.

As mentioned above, some embodiments associate two or more virtual-network MFNs with each SaaS provider's datacenter. SaaS is a software distribution model in which a third-party provider hosts applications and makes them available to customers over the Internet. SaaS removes the need for organizations to install and run applications on their own computers or in their own datacenters. This eliminates the expense of hardware acquisition, provisioning and maintenance, as well as software licensing, installation and support. Also, rather than purchasing software to install, or additional hardware to support it, customers subscribe to a SaaS offering. Generally, they pay for this service on a monthly basis using a pay-as-you-go model. Transitioning costs to a recurring operating expense allows many businesses to exercise better and more predictable budgeting. Users can also terminate SaaS offerings at any time to stop those recurring costs.

SaaS offer high scalability, which gives customers the option to access more, or fewer, services or features without a need to provision or buy more computers. When there is a need to update the software, rather than purchasing new version or update the version the customer own, customers can rely on a SaaS provider to automatically perform updates and patch management. This further reduces the burden on in-house IT staff. Since SaaS applications are delivered over the Internet, users can access them from any Internet-enabled device and location. These advantages have made SaaS a very popular alternative to packaged software that is installed on customer premises using customer hardware. The SaaS providers may host the service on one or more servers in its private datacenter(s) or on one or more servers residing in one or more regions in the public cloud.

Typically, the SaaS provider is identified by the domain name of its service (e.g. www.myworkday.com). Often, there is a different domain name associated with the servers that run public web page of the SaaS provider (www.workday.corn) than the ones that runs the SaaS application (www.myworkday.com). This domain name can be resolved through a DNS query to provide the IP address of the SaaS application server.

In case there are multiple servers, the DNS server may return a different IP address to two different requests that can be associated with different servers. The logic of the different name is from a location basis. If the SaaS provider has several regions in the world where it owns server, each requester will get back an IP server that is closer to it. Inside the same region, the DNS service can still select different servers according to a load balancing point of view; the IP address that is returned is associated with a different server in the same region. This latter case will return IP addresses for different servers who usually share the same IP subnet.

The controller cluster of the virtual network in some embodiments keeps a table of known IP SaaS addresses. When the virtual network gets packets from a customer, the destination IP can be of three different types. First, the packet can be associated with a private location of the entity (i.e., has a destination address in the private IP space of the entity). In this situation, the virtual network in some embodiments routes the packets to the corresponding compute node of the entity that is associated with the packet's destination address.

Second, the packet has a destination addressed that is a public (not private) IP address that is not known to virtual network. These IP addresses are referred to as generic public IP addresses. The virtual network in some embodiments sends such a packet to the Internet from the ingress virtual network node. Third, the packet has a destination addressed that is a public (not private) IP address known to virtual network to be an IP address of a SaaS provider. Such IP addresses are referred to as SaaS IP addresses. In some embodiments, such a packet will be routed from a first virtual-network node (e.g., first CFE of a first MFN) to a second virtual-network node (e.g., second CFE of a second MFN) from where it is provided to the SaaS IP in the shortest possible way in some embodiments.

FIG. 26 illustrates a process 2600 used by the central controller cluster of some embodiments to define routes for multi-homed SaaS providers. This process identifies the various IP addresses associated with a SaaS service and identifies the shortest possible routes from different compute end nodes to one or more SaaS provider datacenters. As shown, the process 2600 starts (at 2605) in some embodiments when the controller cluster receives a SaaS domain name list. In some embodiments, the SaaS domain list is provided by the administrator of the public cloud virtual network provider, while in other embodiments this list is provided by an administrator of the entity for which the public-cloud virtual network is defined by the virtual network provider. The table below provides an example of such a SaaS list.

Name

1.
login.adaptiveinsights.com

2.
adobeid-na1.services.adobe.com

3.
athenanet.athenahealth.com

4.
login.bws.birst.com

5.
account.box.com

6.
centrify.com

7.
identity.citrix.com

8.
login.constantcontact.com

9.
account.docusign.com

10.
login.github.com

11.
secure.gooddata.com

12.
app.hubspot.com

13.
login.huddle.net

14.
hub.insidesales.com

15.
login.marketo.com

16.
system.netsuite.com

17.
login.newrelic.com

18.
login.microsoftonline.com

19.
login.okta.com

20.
login.oracle.com

21.
myapps.paychex.com

22.
login.salesforce.com

23.
servicemax.cloudforce.com

24.
hi.service-now.com

25.
auth.tableausoftware.com

26.
login.ultimatesoftware.com

27.
support.veeva.com

28.
login.xero.com

29.
www.zendesk.com

At 2610, the controller cluster stores the SaaS domain list in a database. In some embodiments, this database is accessible through one or more interfaces (e.g., web server interface and/or API interface) to administrators of the virtual network provider and/or of an entity (e.g., a tenant) for which the virtual network is deployed. Through this interface, an administrator can add or remove SaaS providers and/or associated domain names to the list.

Next, at 2615, the controller cluster learns as many possible IP addresses of SaaS servers associated with the domain on its list of domain names. To that end, the controller cluster in some embodiments directs different measurement agents 205 in the public clouds (that are deployed by the VNP for one or more virtual networks deployed over different public clouds) to execute DNS query for each domain name on the list. Such a query is repeated periodically (e.g. every 30 minutes). The measurement agents 205 transfer back (at 2615) to the controller cluster the IP addresses that they learn for each domain name.

Different measurement agents may return different IP addresses as many SaaS are using geographical DNS service to match an adjacent server with the client. The SaaS providers typically use Authoritative DNS Servers that have lists of SaaS servers and their locations. When such a DNS server gets a DNS request, it receives the measurement agent's IP address, and uses this IP address to Geo-IP map to identify the location of the measurement agent and returns the IP address of the “best” server for the measurement agent. In some embodiments, the measurement agent also provides the IP address of an end-compute node of the virtual network, and the DNS server used by the SaaS provider provides an IP address based on the end-compute node's IP address.

The controller cluster stores (at 2620) in a database the returned IP addresses along with their associated domain names. When at least some number of IPs (e.g., 5) belong to the same IP subnet (e.g., class C subnet that includes 255 or less different addresses), the controller cluster adds the subnet itself to the database. In some embodiments, this database is accessible through one or more interfaces (e.g., web server interface and/or API interface) to administrators of the virtual network provider and/or of an entity (e.g., a tenant) for which the virtual network is deployed. Through this interface, an administrator can add or remove IP addresses. This interface also allows the addition/removal of records associated with domain names that are added/removed by an administrator. Also, in some embodiments, the controller cluster purges IP addresses that are not reported as being used for a duration of time (e.g., every day, every several days, every week or every several weeks, etc.).

After 2620, the central controller identifies (at 2625) for each reported IP address that is received from the reporting measurement agents in one or more public clouds (reporting regions), a set of public clouds (nearby regions) that are near (i.e., within a threshold distance of) the reporting region. In some embodiments, the nearness of two regions are determined in terms of network distances that are measured separately between the regions. In some embodiments, the process 2600 uses third party DNS services to identify an approximate location for each IP address, and then uses the identified locations of the IP addresses to quantify a distance between two IP addresses. The list of the set of regions identified for all the reported IP address is referred to as IP vicinity report. When such operation is not done, the IP vicinity report will define all the virtual network regions as being near each IP address.

At 2630, the central controller provides the IP vicinity report to the deployed measurement agents 205 that are deployed by the VNP for one or more virtual networks deployed over different public clouds. Each measurement agent then periodically measures (e.g., once every several minutes or several hours) the distance between the measurement agent and each SaaS provider IP address that is identified as being near the measurement agent in the IP vicinity report. In some embodiments, the measurement agent computes this distance to an IP address in terms of the delay for initiating a TCP connection with a server at this IP address. When the server, having this IP address is responding, the time to that response is measured. Once a first response is accounted, the measurement agent actively terminates the TCP connection in some embodiments. In some embodiments, the measurement agent also counts the number of successful TCP connection events and/or lost packets. The measurement agent in other embodiments uses other measurement techniques, such as any one of the measurement techniques that were described above.

At 2635, the controller cluster receives the distance measurements from the measurement agents. Next, at 2640, the controller cluster uses the returned measurements (e.g., the delay and loss numbers reported from each measurement agent) to identify routes to each SaaS provider (e.g., to each SaaS IP address) from each possible ingress MFN and/or from each possible MMCN. To identify the routes, the controller cluster performs shortest path route-identification process in some embodiments that relies on the weight values that are computed based on the measurements to the different SaaS IP addresses, between different MFNs and to the different MMCNs.

After identifying these routes, the controller cluster supplies (at 2645) these routes to the MMCN edge nodes and the MFNs. For instance, in some embodiments, the controller cluster provides forwarding records (e.g., routing records that specify the next hop, routing records that specify the virtual network egress node, etc.) to the MMCN edge nodes and to the MFN CFEs. By using these forwarding records to perform their routing operations, the particular MMCN edge nodes and MFN CFEs implement the optimal routes defined (at 2640) by the controller cluster. In some embodiments, the controller cluster supplies new routing records to the MMCN edge nodes and the MFN CFEs whenever it identifies new routes.

In some embodiments, the SaaS IP addresses that are discovered by the above process are assumed to have a zero routing distance to the virtual network node to which they connect (i.e., are assumed to be virtually located in a public cloud region of the virtual network). In other embodiments, the routing links between public cloud regions and SaaS IP addresses have weights associated with them (as reflected in the example of FIG. 25), and these weights reflect the cost (e.g., measured delay and/or loss) associated with the path from those public cloud regions to the SaaS IP addresses. Under this approach, the best regions to connect to a particular IP address are the regions from which the computed weight values (i.e., the cost measured in terms of packet delay and loss) are small.

One rational for associating a SaaS IP address with more than one MFN CFE in more than one public cloud region is that the distance of the SaaS server to multiple regions is much smaller than the typical distance between regions. In addition, it might cost less to route traffic that is originating in one public cloud so it will stay till the egress node in the same cloud. In this case, the controller cluster in some embodiments binds each SaaS IP to at least one region in each public cloud as long as the cost (e.g., the delay and loss) from the nearest region is below some cost (e.g., the delay and loss) threshold. When the route identification process needs to calculate a shortest path to a certain IP address, it first looks to which regions this IP address is bound, then it computes the shortest path from each egress node to the bound region. In some embodiments, the routing tables themselves in the routers do not need to include the external IP address as the data message will be carried in the tunnels until the destination egress node, which then will look up to the IP address in the tunnel.

As mentioned above, the computed weight value in some embodiments accounts for the cost of packet delay and/or loss in a link between two public clouds regions, between a public cloud region and a SaaS provider datacenter, or between a public cloud region and a tenant's compute end node. In other embodiments, the computed weight value for each such link is computed in terms of other types of parameters, such as the data throughput costs that are charged by the public cloud providers and/or the compute cost (for the compute elements used to implement the MFN components in the public cloud) that are charged by the public cloud providers.

To connect a corporate client in one branch to a corporate server in a second branch, a connection would go through the cloud forwarding elements (CFEs) of at least two public cloud managed forwarding nodes (MFNs) associated with the respective branch locations. In some embodiments, each public cloud MFN acts as a cloud relay that can perform TCP split optimization to help reduce the download times of large files. To improve with small file downloads, some embodiments implement novel TCP split improvements, such as using predefined pools of threads to handle SYN requests and predefined pools of connections to forward SYN requests in the cloud relays. These improvements are kernel-based (e.g., Linux kernel based) improvements in some embodiments. In some embodiments, the TCP split optimizing process is referred to below as K-split as it uses kernel sockets to avoid the penalties that stem from the numerous system calls of user-mode implementations.

The virtual network of some embodiments takes advantage of the fairness optimizations among tenants that are offered by the public clouds. In some embodiments, the virtual network provider is a paying tenant of the public clouds. As a paying tenant, within the bandwidth allocated to it (e.g., 2 Gbps), the virtual network provider does not need to provide fairness related to other cloud tenants. This is further emphasized by the fact that all clouds allow the VNP to send unrestricted UDP within its bandwidth budget. Accordingly, the virtual networks of some embodiments use a more aggressive congestion control within the cloud, between pairs of cloud relays (used for traversing the public clouds to connect two external compute nodes) without changing the configuration or set up of any tenant machines outside of the public cloud. This approach improves the virtual network performance, and in a counter intuitive way yields to more fairness between short-distance and long-distance TCP flows.

In some embodiments, the virtual network's performance can be improved when the routing goes through multiple public clouds instead of one. In some cases, a third TCP cloud relay can also improve performance, but this improvement can be negligible when using aggressive congestion controls. The optimized virtual WAN approach of some embodiments can reduce file download times by half versus current end-to-end approaches. Some embodiments also use redundant cloud relays and optimize across these redundant relays to obtain multiple paths through the public clouds.

FIG. 27 illustrates an example of an optimized virtual network 2700 that relies on two cloud relays that perform TCP split optimization. In this example, an end-host 2705 in Frankfurt sends traffic to an end-host 2710 in New York. The host 2705 is referred to below as the server while the host 2710 is referred to as the client. Absent the virtual network of FIG. 27, the default data transport mechanism for forwarding traffic from host 2705 to host 2710 gets routed along the BGP path between the two. As shown in FIG. 28, this path would connect the ISP 2815 of host 2705 to the ISP 2820 of host 2710 via one or more intermediate ISPs 2825. The modulation of traffic from host 2705 to host 2710 in this example is via TCP congestion control, which reacts to network conditions on the path (congestion, freed capacity) by adaptively adjusting the sending rate.

Instead of using the ISPs 2815-2825 of hosts 2705 and 2710, the optimized virtual network of FIG. 27 sends the traffic from the host 2705 directly to a geographically-close MFN 2730 in a Munich datacenter 2760 of a public cloud provider that also has a datacenter 2765 in New York. In this example, the traffic from the host 2705 then traverses the infrastructure of the public cloud provider to reach an egress MFN 2735 in this provider's New York datacenter 2765. From this MFN, the traffic leaves the public cloud to reach the client host 2710.

The optimized virtual network 2700 of FIG. 27 splits the TCP connection between the server and client hosts 2705 and 2710 into three separate connections, each employing TCP (e.g., CUBIC) congestion control. These three connections are the connection 2740 between the server host 2705 and the ingress MFN 2730, the connection 2745 between the two MFNs 2730 and 2735, and the connection between the MFN 2735 and the client host 2710. By connecting the branch location of each compute end node 2705 and 2710 to an optimal public cloud relay (e.g., closest relay, the relay with the fastest connection to the end node, etc.), and splitting the TCP connection at each of these relays, the approach of FIG. 27 substantially improves the large-file download times.

However, this approach in some instances does not sufficiently improve the download times of small files. To improve with small file downloads, some embodiments implement novel TCP split improvements, such as using predefined pools of threads to handle SYN requests and predefined pools of connections to forward SYN requests in the cloud relays. However, before describing these improvements, FIGS. 29-32 present examples that illustrate the download times of files under the split optimization approach of FIG. 27.

In the examples illustrated in FIGS. 29-32, a client machine 2905 in one corporate location requests content from a server machine 2910 in another corporate location. Each requested packet has a maximum segment size (MSS). These requests are done using HTTP over TCP, and the initial TCP window size is one MSS, and that there are no losses. Also, in these examples, R_Cand R_Sare the client-side relay 2915 and server-side cloud relay 2920, respectively.

FIG. 29 illustrates the ideal clean-slate approach, where the request for the content would go through directly, triggering the transmission of all response packets. Under this imaginary approach, no TCP handshake is performed between any two hops along the path. The time-to-first-byte (TTFB) is just one round-trip time (RTT), which is the lowest possible TTFB. In this example, the content is downloaded in three packets. The download time is barely higher as the second and third response packets are received by the client machine 2905 shortly after getting the first response packet.

FIG. 30 illustrates the case where the client and server machines 2905 and 2910 establish an end-to-end connection by performing a three-way TCP handshake. Again, the content in this example is downloaded in three packets. As shown, this approach adds one RTT delay to TTFB as compared to the ideal TTFB of FIG. 29. In addition, waiting for one RTT for the client's ACK after receiving the first response packet adds further delays to the overall download (i.e., to the time it takes to receive the three response packets). The server sends the third packet in this example before it receives the ACK for the second packet from the client because the client and server use CUBIC TCP congestion control, which increases the TCP window size non-linearly to allow multiple packets to be sent in a TCP window before the client acknowledges receiving an earlier packet (in this case the second packet).

FIG. 31 illustrates a TCP-split approach of some embodiments of the invention. Under this approach, the client- and server-side cloud relays 2915 and 2920 act as TCP connection endpoints by performing TCP split operations. This approach results in three sets of TCP three-way handshakes, which, as shown, improves the overall download time for a large flow as it decreases the time that the TCP endpoints (e.g., the server 2910) receives ACKs for their SYN-ACKs and packets. In FIG. 31, the client and server again use CUBIC TCP congestion control, which increases the TCP window size non-linearly to allow multiple packets to be sent in a TCP window before the client acknowledges receiving an earlier packet. In this example, the server can send packets three through seven before receiving the acknowledgment that the client has received the second packet.

FIG. 32 illustrates an example that shows that when the client- and server-side cloud relays 2915 and 2920 perform TCP split operations, the TTFB and overall download time are adversely affected for small flows. Specifically, it shows that TTFB for the client 2905 to receive the first packet and the overall download time for the client to get a three-packet flow are slower than the TTFB and overall download time of the end-to-end TCP connection of FIG. 30.

FIG. 32 illustrates five types of delays, with each type being labeled 1-5. The first delay (delay 1) accounts for the time that a TCP endpoint has to wait to receive an ACK after it has replied to a SYN with a SYN-ACK (i.e., to have its SYN-ACK acknowledged). This delay occurs between each pair of adjacent TCP endpoints in FIG. 32. The instance of this delay between the client machine 2905 and the cloud relay 2915 is annotated in FIG. 32 with the legend Δ_C.

The second delay (delay 2) accounts for the time that the TCP endpoint has to create a duplicate TCP connection thread to perform the three-way handshake with a subsequent TCP endpoint in the series of successive TCP connection endpoints from the client machine 2905 to the server machine 2910. This second delay is referred to as the fork delay as the expression fork is commonly used to refer to the act of creating a duplicate thread.

The third delay (delay 3) accounts for the time that is needed for two cloud relays 2915 and 2920 to set up a TCP connection. This delay is referred to as the connection setup delay. The fourth delay (delay 4) accounts for the time that the cloud relay 2915 receives second and third packets after the cloud relay 2920 receives these packets and waits until it receives an acknowledgment that the cloud relay 2915 that it has received the first packet. This delay is referred to as the TCP-window incrementing delay. The fifth delay (delay 5) is the time that the server 2910 waits for the cloud relay 2920 to acknowledge receiving the first packet. This delay is annotated in FIG. 32 with the legend Δ_S.

As further described below, each of the delays illustrated in FIG. 32 can be resolved with novel kernel-based TCP split improvements, with the exception of the client- and server-side delays Δ_Cand Δ_S, as some embodiments do not change machine or appliance configurations at the client and server locations. However, as mentioned below, some embodiments minimize these delays Δ_Cand Δ_Sby having the cloud relays R_Cand R_Sclose to the client and server locations.

FIGS. 33-37 illustrate four combinable TCP-split improvements that can be used to eliminate delays 1-4 identified in FIG. 32. Specifically, FIG. 33 illustrates the use of an early-SYN approach to remove the SYN-ACK and ACK delays (delay 1 in FIG. 32), FIG. 34 illustrates the use of a thread pool to remove fork delays (delay 2 in FIG. 32), FIG. 35 illustrates the use of a connection pool to eliminate the connection setup delay (delay 3 in FIG. 32), and FIG. 36 illustrates the use of a Turbo-Start TCP eliminating the TCP-window incrementing delay (marked as delay 4 in FIG. 32).

The early-SYN approach of FIG. 33 sends a SYN packet from the cloud relay 2915 to the cloud relay 2920 after the cloud relay 2915 receives a SYN packet from the client machine 2905. The cloud relay 2915 does this without waiting for the three-way handshake to complete with the client machine 2905. The virtual network MFN that acts as a cloud relay (i.e., acts as an intermediate TCP-split node) captures this first SYN packet and triggers the start of a new connection to the next node. This allows the cloud relay 2915 to establish the two legs of a split connection in parallel. This approach removes the SYN-ACK and ACK delays, marked as delay 1 in FIG. 32. However, this approach does not remove the fork delay 2 as the cloud relay 2915 has to waste time to start a thread for handling the forwarding of the SYN packet to the cloud relay 2920.

To eliminate the fork delay, the cloud relays 2915 and 2920 create a pool of reusable kernel threads for processing outgoing SYN packets after receiving incoming SYN packets. FIG. 34 illustrates how this thread pool approach eliminates the time that it takes to create new kernel threads for new split connections. The creation of new kernel threads for each new split connection is time-consuming and adds greatly to the connection jitter. Some outliers may take tens of milliseconds, greatly hurting performance. For small files/objects used by a time-sensitive application, this jitter may even nullify the benefit of layer 4 optimized virtual network.

To mitigate this problem, each cloud relay in some embodiments creates a pool of reusable kernel threads to process setting up a new split connection with a subsequent TCP endpoint in a path from a source machine to a destination machine. As mentioned above, the source machine and/or destination machine can reside in multi-machine nodes, such as branch offices or datacenters. In some such cases, the TCP connection, and the path through the public cloud datacenters, are between gateways at the multi-machine nodes. However, for purposes of brevity, the discussion in this document at times refers to paths between source and destination machines even when the paths terminate at the gateways when viewed from the perspective of the virtual network defined over one or more public clouds.

The reusable kernel threads are sleeping threads, awaiting to be assigned to new tasks. The fork delays of FIG. 33 are eliminated in FIG. 34, because, as shown, the intermediate cloud relay 2915 can use one of the previously created threads to send to the next cloud relay 2920 the SYN packet immediately upon receiving the SYN packet from a previous hop. In FIG. 34, this SYN packet is designated as a SYN′ packet because it does not initiate a three-way handshake between the two cloud relays 2915 and 2920. Instead, it just directs the cloud relay 2920 to forward the SYN packet to the destination machine 2910 (or destination machine's branch or datacenter location) and perform a three-way handshake with this destination machine (or with a gateway at this machine's branch or datacenter location). In some embodiments, the SYN′ packet is a special packet that encapsulates the SYN packet in another packet sent to the next hop cloud relay. The next hop cloud relay will know that it needs to extract this SYN packet from the SYN′ packet and use this the SYN packet to perform a three-way handshake with the destination machine 2910 or the destination machine's branch or datacenter location.

FIG. 35 illustrates the connection pool approach, which uses pre-established connections between cloud relays to eliminate the delay associated with setting up connections between first and second cloud relays 2915 and 2920 along a path from a source machine to a destination machine through two public clouds. Using pre-established connection between these two cloud relays improve the performance over long connections, i.e., those where the RTT between the two cloud relays dominates. The goal is to negate the delay of the long three-way handshake.

To achieve this goal, some embodiments preemptively connect each particular MFN that can serve as a TCP-split optimization ingress/egress node to each other MFN (1) that can serve for the particular MFN a TCP-split optimization egress/ingress node and (2) that is far from the particular MFN. In some embodiments, the TCP-split optimization operations are performed by the MFN's TCP optimization engine 220. The TCP optimization engine 220 in some embodiments executes on the same machine (e.g., same VM or container) as the cloud forwarding element 235 (CFE) of the MFN.

Some embodiments create a pool of pre-established connections (a connection pool) between each pair of distant relays, and replenish this pool each time a connection is used. This approach eliminates the delay between sending SYN packet from a cloud relay (first relay) and then sending the ACK from that relay (the first relay) after receiving the SYN-ACK from the other cloud relay (the second relay). This delay is eliminated because the first relay with its SYN packet identifies the previously-created connection that it has selected from the connection pool for this communication session with the second relay.

FIG. 36 illustrates the use of a Turbo-Start TCP to eliminate the TCP-window incrementing delay, marked as delay 4 in FIG. 32. As mentioned above, existing TCP congestion control processes increases the TCP window size non-linearly to allow more and more packets to be sent in a TCP window before the client acknowledges receiving an earlier packet. In these existing TCP congestion control processes (e.g., a CUBIC TCP process), the TCP window size is a function of time since the last congestion event, with the inflection point set to the window size prior to the event.

Since the cloud provider is responsible for maintaining fairness between different tenants, there is no need to maintain TCP-friendliness with legacy or default TCP congestion control mechanisms. Furthermore, congestion is less of an issue within the cloud. Hence, some embodiments configure larger than normal initial congestion window (CWND) and receive window (RWIN) on layer-4 optimized cloud relays. As shown in FIG. 36, these larger windows allow the cloud relay 2920 to forward the second and third packets immediately to the cloud relay 2915 without for the ACK from the cloud relay 2915 to acknowledge receiving the first packet. In addition, the Turbo-Start TCP process of some embodiments increases the socket buffers for the cloud relays, so that memory size does not limit the performance of the intra-cloud flows. Some embodiments do not change the CWND used on any Internet-facing flows, and therefore do not affect any multi-computer nodes (e.g., branch offices or datacenters).

In addition to using the four improvements of FIGS. 33-36 that avoid delays 1-4 in performing TCP-split operations, each cloud relay of some embodiments uses a novel kernel module (called K-split) that implements kernel-based TCP split operations rather quickly. K-split is implemented in Ubuntu in some embodiments. In addition, K-split enables utilizing easily-deployable commodity VMs, as well as standard programming APIs (POSIX/Berkeley sockets) in some embodiments.

The decision to implement K-split in kernel mode is advantageous in that it allows the virtual network (1) to take advantage of resources only available in the kernel, such as Netfilter, and (2) to avoid the penalties that stem from numerous system calls. Implementing K-split in the kernel also eliminates the redundant transitions to and from user space and avoid gratuitous system calls. The decision to implement the components of K-split in the kernel is further made easy by the fact that all socket APIs have kernel counterparts. Also, in some embodiments, the in-kernel implementation does not have a kernel API for a scalable I/O event notification mechanism (epoll). Because of this, some embodiments use kernel-threads to service the sockets

In some embodiments, K-split has three components: (1) a source-side first socket that listens for incoming connections, (2) IPtable rules redirect specific TCP packets to the source-side first socket, and (3) a destination-side second socket that connects to the destination or the next hop to the destination, and thus complete the second leg of the split connection. Once both connections are established, the bytes of a single stream are read from one socket, and then forwarded to its peer by one of two handlers, a source-side handler or a destination-side handler. This forwarding happens in both directions. When either connection is terminated via an error or TCP FIN flag, the other connection is gracefully shut down. This means that the bytes in flight (i.e., not yet ACK'ed) will reach their destination, but no new bytes will be sent.

Some embodiments particularly pick the buffer size used to read data from and write data to the sockets. For instance, instead of using smaller 4 KB buffer, some embodiments use 16 KB and 64 KB buffer sizes. To implement Early-SYN, some embodiments use Linux Netfilter hooks, as there is no standard API that enables the capture of the first SYN packet. The added hook captures TCP packets. The headers are then parsed for the destination and the SYN flag. With this information K-split selects a pre-allocated kernel-thread that initiates a connection to the destination or the next hop along the path to the destination. Capturing the SYN allows the relays to establish the two sides of a connection concurrently.

To implement the thread pool, each split connection is handled by two handlers, which are dedicated kernel-threads in some embodiments. Each handler receives from one socket and writes to its peer. Each handler is responsible for one direction of the connection. Some embodiments use blocking send/receive calls with the sockets to keep the implementation simple; this also means that a kernel-thread per active socket is needed. The creation of a new kernel-thread is a costly process, as an outlier may consume several milliseconds, resulting in a jittery behavior. To mitigate this problem and the problem of creating new kernel-threads from interrupt context, some embodiments create a pool of reusable threads to implement the handlers.

Each kernel-thread in this pool is initially waiting in state TASK_INTERRUPTIBLE (ready to execute). When the thread is allocated for a specific handler, two things happen: (1) a function to execute is set and (2) the task is scheduled to run (TASK_RUNNING). When the function terminates, the thread returns to state TASK_INTERRUPTIBLE and back to the list of pending threads, awaiting to be allocated once more. The pool of pre-allocated kernel threads thus removes the overhead of new kernel-thread creation.

To implement pre-established connections, some embodiments use a destination-side second socket. Unlike the source-side first socket, this socket listens for connections from other relays that are initiating new pre-established connections in some embodiments. In order to keep the connection from closing before it is used, the sockets are configured with KEEP_ALIVE. When established, these connections wait for the destination address to be sent from the initiating peer. The destination address is sent over the connection itself. This information is sent in the very first bytes, and all following bytes belong to the forwarded stream. Once the destination address is received, a connection to the destination is established and the second leg of the split connection is established. The streams are forwarded between the sockets just like in the basic design. Some embodiments disable Nagle's Algorithm on these sockets in order to keep the time-to-first-byte low. Without disabling it, the time-to-first-byte in some embodiments increases by a few hundred milliseconds.

Some embodiments control the size of the thread-pool, the destination of a pre-established connections, and their number via proc filesystem (procfs), which is a special filesystem in Linux that presents information about processes and other system information in a hierarchical file-like structure, providing a more convenient and standardized method for dynamically accessing process data held in the kernel than tracing methods or direct access to kernel memory.

With 10K split connections, the memory footprint of socket buffers might exceed the size of the shared L3 cache of some servers. It may be prudent to expand the epoll API to the kernel and thus save the 18 KB of memory per split connection. In addition to extending epoll API, some embodiments try to avoid needless copy of the socket API in the kernel as this API is not zero copy. Also, network I/O is serviced by interrupts. For a virtual machine, this means expensive VM exits. Hence, some embodiments use an SRIOV device and a machine with a CPU that supports Intel's vt-d posted interrupts to achieve near bare-metal performance.

FIG. 37 illustrates K-split module 3700 of a cloud relay that is implemented on a host computer in a public cloud datacenter in some embodiments. As shown, this module includes a K-split processor 3705, IP Table 3710, source-side sockets 3715, destination-side sockets 3720, source-side handlers 3725 and destination-side handlers 3730. The K-split processor registers with a filter 3750 of the host computer's operating system (e.g., registers with Netfilter of the Linux operating system) to receive notification of SYN packets that the host computer receives for the cloud relay.

For each such SYN packets, the K-split processor 3705 uses the SYN packet's attributes (e.g., source and destination IP addresses in the SYN packet's header) to identify a record in the IP table 3710 that specifies a next hop along a route from a source machine to a destination machine through the public clouds. Based on the identified next hop, the K-split processor (1) selects a pre-established connection to the next hop from a set 3760 of pre-established connections that are defined between the K-split's cloud relay and other cloud relays, (2) allocates this the selected connection's socket as the destination-side socket 3720, and (3) selects one of a set 3755 of predefined handler to implement the destination-side handler 3730 to handle the TCP connection (e.g., to pass the SYN packet) to the next hop along the route to the destination. Through the selected connection, packets are exchanged between the destination-side socket 3720 of the K-split module 3700 and the source-side socket 3715 of the K-split module of the next hop cloud relay, or the destination machine when the next hop is the destination machine.

The K-split module 3700 also allocates a source-side socket 3715 to store packets exchanged with the previous hop (e.g., the source machine or the destination-side socket 3720 of the previous hop's cloud relay), and allocates a source-side handler 3725 to handle the TCP connection with the previous hop. The source-side handler processes packets from the previous hop by retrieving these packets from the source-side socket 3715 and storing them in this socket's corresponding destination-side socket 3720. Conversely, the destination-side handler 3730 processes packets from the next hop by retrieving these packets from the destination-side socket 3720 and storing them in this socket's corresponding source-side socket 3715.

FIG. 38 illustrates a process 3800 that the K-split module 3700 of some embodiments performs once it receives a Netfilter interrupt that this filter has captured a first SYN packet. This process 3800 allocates the source- and destination-side sockets and handlers to process the new connection that is being initiated by the new SYN packet. The process 3800 also uses a novel technique to quickly pair the source- and destination-side sockets, by initially pinning the handlers for these two sockets to the same processor core of the host computer so that these handlers can use a common data structure (e.g., a tree) to quickly identifying matching socket pairs for the connection. These allocation and pairing operations will be described by reference to the examples illustrated in FIGS. 39-44.

As shown, the process 3800 starts when the K-split module 3700 receives an interrupt Netfilter interrupt that this filter has captured a first SYN packet. FIG. 39 illustrates that the handling of this interrupt is assigned initially to core 0 of the host computer's processor. This assignment can be random or based on some heuristic. Based on the attributes of this packet (e.g., source and destination IP addresses in the SYN packet's header), the interrupt process running on this core identifies (at 3805) a record in the IP table 3710 that specifies a next hop along a route from a source machine to a destination machine through one or more public cloud datacenters. This process also allocates (at 3805) one of the predefined handlers 3750 to serve as the destination-side handler 3720. This process initially pins this handler to the processor's core 2 by hashing the SYN packet's five tuple (source and destination IP address, source and destination port and protocol), and using this hash to identify a core identifier in a look-up table that associates hash values with core identifiers. The identified core identifier specifies the core to which the destination-side hander should be pinned.

FIG. 40 illustrates the destination-side handler 3720 being initially assigned to core 2. It also illustrates the operations of this handler. As shown in FIGS. 38 and 40, the destination-side handler initially selects (at 3810) a pre-established connection to the next hop to connect to the next hop (e.g., to pass along the received SYN packet to the next hop cloud relay or destination machine). At 3810, the destination-side handler also defines the destination-side socket 3720 as the socket of the pre-established connection.

It also defines (at 3810) a search record that stores the identity of the allocated destination-side socket 3720 and the key associated with this socket. The key in some embodiments is the source and destination IP addresses and source and destination port addresses in the header of the received SYN packet. As further described below, the source- and destination-side handlers in some embodiments use this key to match two search records, which in turn allow them to match source and destination-side sockets. In some embodiments, the destination-side handler 3720 adds (at 3810) the search record that it creates to a local tree structure that is stored in the local cache of core 2 so that this core can quickly search this tree structure subsequently when it needs to find a destination-side search record to match to a source-side search record to match the two sockets.

At 3810, the destination-side handler sends the SYN packet to the destination when the next hop is the destination or sends the SYN′ packet to the next hop cloud relay when the next hop is another cloud relay. When the next hop is the destination machine, the destination-side handler performs three-way handshake with the next hop destination machine to establish the TCP connection with this machine. As mentioned above, when the destination machine is in a branch office or datacenter, the SYN packet is forwarded to the gateway at this location and the destination-side handler forwards the SYN packet to a gateway at this location and sets up the TCP connection with this gateway.

On the other hand, the SYN′ packet to the next hop cloud relay is a special packet that encapsulates the SYN packet in another packet sent to the next hop cloud relay. The next hop cloud relay will know that it needs to extract this SYN packet from the SYN′ packet and use this the SYN packet (1) to perform a three-way handshake with the destination machine when the destination machine is the subsequent hop from the next hop, or (2) to forward the SYN packet in another SYN′ packet to another cloud relay when the other cloud relay is a subsequent hop. The destination-side handler then enters (at 3810) a wait cycle for the source-side handler to pair the two sockets and wake the destination-side handler so that the destination-side handler can start processing packets by moving packets from the destination-side socket to the source-side socket.

At 3815, the process 3800 performs a series of operations to accept the connection from the source. FIG. 41 illustrates these operations falling on core 3 of the host computer's processor. Again, these operations fall on this core randomly or based on some heuristics. As shown, the process (at 3815) (1) allocates a source-side socket 3715, (2) a search structure to store the identity of this socket and its associated key, which again is the source and destination IP addresses and source and destination port addresses in the header of the received SYN packet, and (3) allocates one of the predefined handlers to implement the source-side handler 3725 for this new connection. At 3815, the process assigns the execution of this source-side handler 3725 to core 2 by generating a hash value from the SYN packet's five tuple and using this hash to identify a core identifier in a look-up table that associates hash values with core identifiers. The identified core identifier specifies the core to which the destination-side hander should be pinned. As the same hash functions and look-up tables are used at 3805 and 3815, the source- and destination-side handlers will always be initially pinned to the same core.

FIG. 42 illustrates the source-side handler's operations on core 2. These operations are also illustrated at 3820 of FIG. 38. As shown, the source-side handler uses the search key of the search record allocated at 3815 to find the matching search record (i.e., the destination-side search record with the matching search key) stored in the local search tree. The search record allocated at 3810 contains the identity of the destination-side socket, while the search record allocated at 3815 contains the identity of the source-side socket. Hence, matching these two search records allows the K-split process 3800 to pair two associated source- and destination-side sockets for the new connection. Once the search records are matched, the source-side handler removes the search record from the search tree in order to keep the local tree structure small for quicker subsequent searches.

After matching the sockets, the source-side handler then wakes up the destination-side handler. At this point, both handlers can start to forward packets between the two sockets, with the source-side handler moving packets from the source socket to the destination socket, and the destination-side handler moving packets from the destination socket to the source socket, as illustrated in FIG. 43. Once the sockets have been paired and the handlers start to move packets between the sockets, the handlers are no longer pinned to any one core and can execute on different cores in different clock cycles, as shown in FIG. 44. As mentioned above, the two handlers were initially assigned to the same core as a means to allow them to store and search corresponding search records in the same search tree in order to quickly identify corresponding pairs of sockets. It is much faster to pair two sockets by using one core to search one search structure in the core's local cache than using multiple cores to search one global search structure accessible to all the cores.

Many of the above-described features and applications are implemented as software processes that are specified as a set of instructions recorded on a computer readable storage medium (also referred to as computer readable medium). When these instructions are executed by one or more processing unit(s) (e.g., one or more processors, cores of processors, or other processing units), they cause the processing unit(s) to perform the actions indicated in the instructions. Examples of computer readable media include, but are not limited to, CD-ROMs, flash drives, RAM chips, hard drives, EPROMs, etc. The computer readable media does not include carrier waves and electronic signals passing wirelessly or over wired connections.

In this specification, the term “software” is meant to include firmware residing in read-only memory or applications stored in magnetic storage, which can be read into memory for processing by a processor. Also, in some embodiments, multiple software inventions can be implemented as sub-parts of a larger program while remaining distinct software inventions. In some embodiments, multiple software inventions can also be implemented as separate programs. Finally, any combination of separate programs that together implement a software invention described here is within the scope of the invention. In some embodiments, the software programs, when installed to operate on one or more electronic systems, define one or more specific machine implementations that execute and perform the operations of the software programs.

FIG. 45 conceptually illustrates a computer system 4500 with which some embodiments of the invention are implemented. The computer system 4500 can be used to implement any of the above-described hosts, controllers, and managers. As such, it can be used to execute any of the above described processes. This computer system includes various types of non-transitory machine readable media and interfaces for various other types of machine readable media. Computer system 4500 includes a bus 4505, processing unit(s) 4510, a system memory 4525, a read-only memory 4530, a permanent storage device 4535, input devices 4540, and output devices 4545.

The bus 4505 collectively represents all system, peripheral, and chipset buses that communicatively connect the numerous internal devices of the computer system 4500. For instance, the bus 4505 communicatively connects the processing unit(s) 4510 with the read-only memory 4530, the system memory 4525, and the permanent storage device 4535.

From these various memory units, the processing unit(s) 4510 retrieve instructions to execute and data to process in order to execute the processes of the invention. The processing unit(s) may be a single processor or a multi-core processor in different embodiments. The read-only-memory (ROM) 4530 stores static data and instructions that are needed by the processing unit(s) 4510 and other modules of the computer system. The permanent storage device 4535, on the other hand, is a read-and-write memory device. This device is a non-volatile memory unit that stores instructions and data even when the computer system 4500 is off. Some embodiments of the invention use a mass-storage device (such as a magnetic or optical disk and its corresponding disk drive) as the permanent storage device 4535.

Other embodiments use a removable storage device (such as a floppy disk, flash drive, etc.) as the permanent storage device. Like the permanent storage device 4535, the system memory 4525 is a read-and-write memory device. However, unlike storage device 4535, the system memory is a volatile read-and-write memory, such a random access memory. The system memory stores some of the instructions and data that the processor needs at runtime. In some embodiments, the invention's processes are stored in the system memory 4525, the permanent storage device 4535, and/or the read-only memory 4530. From these various memory units, the processing unit(s) 4510 retrieve instructions to execute and data to process in order to execute the processes of some embodiments.

The bus 4505 also connects to the input and output devices 4540 and 4545. The input devices enable the user to communicate information and select commands to the computer system. The input devices 4540 include alphanumeric keyboards and pointing devices (also called “cursor control devices”). The output devices 4545 display images generated by the computer system. The output devices include printers and display devices, such as cathode ray tubes (CRT) or liquid crystal displays (LCD). Some embodiments include devices such as a touchscreen that function as both input and output devices.

Finally, as shown in FIG. 45, bus 4505 also couples computer system 4500 to a network 4565 through a network adapter (not shown). In this manner, the computer can be a part of a network of computers (such as a local area network (“LAN”), a wide area network (“WAN”), or an Intranet, or a network of networks, such as the Internet. Any or all components of computer system 4500 may be used in conjunction with the invention.

Some embodiments include electronic components, such as microprocessors, storage and memory that store computer program instructions in a machine-readable or computer-readable medium (alternatively referred to as computer-readable storage media, machine-readable media, or machine-readable storage media). Some examples of such computer-readable media include RAM, ROM, read-only compact discs (CD-ROM), recordable compact discs (CD-R), rewritable compact discs (CD-RW), read-only digital versatile discs (e.g., DVD-ROM, dual-layer DVD-ROM), a variety of recordable/rewritable DVDs (e.g., DVD-RAM, DVD-RW, DVD+RW, etc.), flash memory (e.g., SD cards, mini-SD cards, micro-SD cards, etc.), magnetic and/or solid state hard drives, read-only and recordable Blu-Ray® discs, ultra-density optical discs, any other optical or magnetic media, and floppy disks. The computer-readable media may store a computer program that is executable by at least one processing unit and includes sets of instructions for performing various operations. Examples of computer programs or computer code include machine code, such as is produced by a compiler, and files including higher-level code that are executed by a computer, an electronic component, or a microprocessor using an interpreter.

While the above discussion primarily refers to microprocessor or multi-core processors that execute software, some embodiments are performed by one or more integrated circuits, such as application specific integrated circuits (ASICs) or field programmable gate arrays (FPGAs). In some embodiments, such integrated circuits execute instructions that are stored on the circuit itself.

As used in this specification, the terms “computer”, “server”, “processor”, and “memory” all refer to electronic or other technological devices. These terms exclude people or groups of people. For the purposes of the specification, the terms display or displaying means displaying on an electronic device. As used in this specification, the terms “computer readable medium,” “computer readable media,” and “machine readable medium” are entirely restricted to tangible, physical objects that store information in a form that is readable by a computer. These terms exclude any wireless signals, wired download signals, and any other ephemeral or transitory signals.

While the invention has been described with reference to numerous specific details, one of ordinary skill in the art will recognize that the invention can be embodied in other specific forms without departing from the spirit of the invention. For instance, several of the above-described examples illustrate virtual corporate WANs of corporate tenants of a virtual network provider. One of ordinary skill will realize that in some embodiments, the virtual network provider deploys virtual networks over several public cloud datacenters of one or more public cloud providers for non-corporate tenants (e.g., for schools, colleges, universities, non-profit entities, etc.). These virtual networks are virtual WANs that connect multiple compute endpoints (e.g., offices, datacenters, computers and devices of remote users, etc.) of the non-corporate entities.

Several embodiments described above include various pieces of data in the overlay encapsulation headers. One of ordinary skill will realize that other embodiments might not use the encapsulation headers to relay all of this data. For instance, instead of including the tenant identifier in the overlay encapsulation header, other embodiments derive the tenant identifier from the addresses of the CFEs that forward the data messages, e.g., in some embodiments in which different tenants have their own MFNs deployed in the public clouds, the tenant identity is associated with the MFN's that process the tenant messages.

Also, several figures conceptually illustrate processes of some embodiments of the invention. In other embodiments, the specific operations of these processes may not be performed in the exact order shown and described in these figures. The specific operations may not be performed in one continuous series of operations, and different specific operations may be performed in different embodiments. Furthermore, the process could be implemented using several sub-processes, or as part of a larger macro process. Thus, one of ordinary skill in the art would understand that the invention is not to be limited by the foregoing illustrative details, but rather is to be defined by the appended claims

Claims

1. A method of performing a TCP (Transport Control Protocol) connection split operation, the method comprising: at a first computer that is along a path traversed from a source machine to a destination machine: creating, before receiving a connection request from the source machine for a TCP connection to the destination machine, a plurality of pre-established TCP connections with a second computer that is along the path traversed from the source machine to the destination machine;receiving, from the source machine, the TCP connection request for a connection to the destination machine;selecting a particular TCP connection from the plurality of pre-established TCP connections; andusing the particular TCP connection with the second computer to relay the received connection request from the source machine to the destination machine.
2. The method of claim 1, wherein the connection request is a SYN packet for a three-way TCP handshake, andusing the particular connection to relay the received TCP connection request comprises forwarding the SYN request in a packet to the second computer as part of the particular connection between the first and second computers without performing a three-way handshake between the first and second computers after the connection request is received.
3. The method of claim 2 further comprising after forwarding the SYN request to the second computer, which is a next hop along the path, completing the three-way handshake with a prior hop along the path from the source machine to the destination machine, in order to establish a source-side connection between the first computer and the prior hop.
4. The method of claim 1, wherein the cloud datacenters are public cloud datacenters.
5. The method of claim 1, wherein each of the first and second machines is a virtual machine or container.
6. The method of claim 1, wherein creating the plurality of pre-established TCP connections comprises establishing, before receiving the connection request, two or more pluralities of connections between the first computer and two or more other computers deployed in cloud datacenters and operating as cloud relays.
7. The method of claim 1 further comprising: instantiating, before receiving the connection request, a plurality of connection-handling threads for processing connection requests as the connection requests are received;using a previously instantiated connection-handling thread, to relay the received connection request from the source machine to the destination machine.
8. The method of claim 7 further comprising: wherein using the previously instantiated connection handling thread comprises assigning one of the instantiated connection-handling threads to relay the connection request to the second computer.
9. The method of claim 7 further comprising: wherein using the previously instantiated connection handling thread comprises assigning one of the instantiated connection-handling threads to process the connection request at the first computer to establish a source-side connection between the first computer and a prior hop along the path from the source machine to the destination machine.
10. The method of claim 7, wherein the threads operate in the kernel space of an operating system executing on the first computer.
11. A non-transitory machine readable medium storing a program for performing a TCP (Transport Control Protocol) split operation on a first computer that is along a path traversed from a source machine to a destination machine, the program for execution by at least one processing unit of the first computer that is along the path with a second computer, the program comprising sets of instructions for: creating, before receiving a connection request from the source machine for a TCP connection to the destination machine, a plurality of pre-established TCP connections with the second computer;receiving, from the source machine, the connection request for a TCP connection to the destination machine;selecting a particular TCP connection from the plurality of pre-established TCP connections; andusing the particular TCP connection with the second computer to relay the received connection request from the source machine to the destination machine.
12. The non-transitory machine readable medium of claim 11, wherein the connection request is a SYN packet for a three-way TCP handshake, andthe set of instructions for using the particular connection to relay the received TCP connection request comprises a set of instructions for forwarding the SYN request in a packet to the second computer as part of the particular connection between the first and second computers without performing a three-way handshake between the first and second computers after the connection request is received.
13. The non-transitory machine readable medium of claim 12, wherein the program further comprises a set of instructions for completing, after forwarding the SYN request to the second computer, the three-way handshake with a prior hop along the path from the source machine to the destination machine, in order to establish a source-side connection between the first computer and the prior hop.
14. The non-transitory machine readable medium of claim 11, wherein both the first and second computers deployed in public cloud datacenters and operating as cloud relays that perform TCP connection splits between machines operating outside of the cloud datacenters, the machines including the source and destination machines.
15. The non-transitory machine readable medium of claim 11, wherein each of the first and second machines is a virtual machine or container.
16. The non-transitory machine readable medium of claim 11, wherein the set of instructions for creating the plurality of pre-established TCP connections comprises a set of instructions for establishing, before receiving the connection request, two or more pluralities of TCP connections between the first computer and two or more of other computers deployed in cloud datacenters and operating as cloud relays.
17. The non-transitory machine readable medium of claim 11, wherein the program further comprises sets of instructions for: instantiating, before receiving the connection request, a plurality of connection-handling threads for processing connection requests as the connection requests are received;using a previously instantiated connection-handling thread, to relay the received connection request from the source machine to the destination machine.
18. The non-transitory machine readable medium of claim 17, wherein the set of instructions for using the previously instantiated connection handling thread comprises a set of instructions for assigning one of the instantiated connection-handling threads to relay the connection request to the second computer.
19. The non-transitory machine readable medium of claim 17, wherein the set of instructions for using the previously instantiated connection handling thread comprises a set of instructions for assigning one of the instantiated connection-handling threads to process the connection request at the first computer to establish a source-side connection between the first computer and a prior hop along the path from the source machine to the destination machine.
20. The non-transitory machine readable medium of claim 17, wherein the threads operate in the kernel space of an operating system executing on the first computer.

CLAIM OF BENEFIT TO PRIOR APPLICATIONS

This application is a continuation application of U.S. patent application Ser. No. 16/405,986, filed May 7, 2019, now published as U.S. Patent Publication 2019/0268421. U.S. patent application Ser. No. 16/405,986 is a continuation application of U.S. patent application Ser. No. 16/252,696, filed Jan. 20, 2019, now issued as U.S. Pat. No. 11,089,111. U.S. patent application Ser. No. 16/252,696 is a continuation in part of U.S. patent application Ser. No. 16/192,774, filed Nov. 15, 2018, now issued as U.S. Pat. No. 10,959,098. U.S. patent application Ser. No. 16/192,774 is a continuation in part of U.S. patent application Ser. No. 15/972,083, filed May 4, 2018, now issued as U.S. Pat. No. 11,005,684. U.S. patent application Ser. No. 15/972,083 claims the benefit of U.S. Provisional Patent Application 62/566,524, filed Oct. 2, 2017. U.S. Patent Publication 2019/0268421 is incorporated herein by reference.

US Referenced Citations (1008)

Number	Name	Date	Kind
5652751	Sharony	Jul 1997	A
5909553	Campbell et al.	Jun 1999	A
6154465	Pickett	Nov 2000	A
6157648	Voit et al.	Dec 2000	A
6201810	Masuda et al.	Mar 2001	B1
6363378	Conklin et al.	Mar 2002	B1
6445682	Weitz	Sep 2002	B1
6744775	Beshai et al.	Jun 2004	B1
6976087	Westfall et al.	Dec 2005	B1
7003481	Banka et al.	Feb 2006	B2
7280476	Anderson	Oct 2007	B2
7313629	Nucci et al.	Dec 2007	B1
7320017	Kurapati et al.	Jan 2008	B1
7373660	Guichard et al.	May 2008	B1
7581022	Griffin et al.	Aug 2009	B1
7680925	Sathyanarayana et al.	Mar 2010	B2
7681236	Tamura et al.	Mar 2010	B2
7751409	Carolan	Jul 2010	B1
7962458	Holenstein et al.	Jun 2011	B2
8051185	Lee	Nov 2011	B2
8094575	Vadlakonda et al.	Jan 2012	B1
8094659	Arad	Jan 2012	B1
8111692	Ray	Feb 2012	B2
8141156	Mao et al.	Mar 2012	B1
8224971	Miller et al.	Jul 2012	B1
8228928	Parandekar et al.	Jul 2012	B2
8243589	Trost et al.	Aug 2012	B1
8259566	Chen et al.	Sep 2012	B2
8274891	Averi et al.	Sep 2012	B2
8301749	Finklestein et al.	Oct 2012	B1
8385227	Downey	Feb 2013	B1
8516129	Skene	Aug 2013	B1
8566452	Goodwin et al.	Oct 2013	B1
8588066	Goel et al.	Nov 2013	B2
8630291	Shaffer et al.	Jan 2014	B2
8661295	Khanna et al.	Feb 2014	B1
8724456	Hong et al.	May 2014	B1
8724503	Johnsson et al.	May 2014	B2
8745177	Kazerani et al.	Jun 2014	B1
8769129	Watsen	Jul 2014	B2
8797874	Yu et al.	Aug 2014	B2
8799504	Capone et al.	Aug 2014	B2
8804745	Sinn	Aug 2014	B1
8806482	Nagargadde et al.	Aug 2014	B1
8855071	Sankaran et al.	Oct 2014	B1
8856339	Mestery et al.	Oct 2014	B2
8964548	Keralapura et al.	Feb 2015	B1
8989199	Sella et al.	Mar 2015	B1
9009217	Nagargadde et al.	Apr 2015	B1
9015299	Shah	Apr 2015	B1
9055000	Ghosh et al.	Jun 2015	B1
9060025	Xu	Jun 2015	B2
9071607	Twitchell, Jr.	Jun 2015	B2
9075771	Gawali et al.	Jul 2015	B1
9100329	Jiang et al.	Aug 2015	B1
9135037	Petrescu-Prahova et al.	Sep 2015	B1
9137334	Zhou	Sep 2015	B2
9154327	Marino et al.	Oct 2015	B1
9203764	Shirazipour et al.	Dec 2015	B2
9225591	Beheshti-Zavareh et al.	Dec 2015	B2
9306949	Richard et al.	Apr 2016	B1
9323561	Ayala et al.	Apr 2016	B2
9336040	Dong et al.	May 2016	B2
9354983	Yenamandra et al.	May 2016	B1
9356943	Lopilato et al.	May 2016	B1
9379981	Zhou et al.	Jun 2016	B1
9413724	Xu	Aug 2016	B2
9419878	Hsiao et al.	Aug 2016	B2
9432245	Sorenson et al.	Aug 2016	B1
9438566	Zhang et al.	Sep 2016	B2
9450817	Bahadur et al.	Sep 2016	B1
9450852	Chen et al.	Sep 2016	B1
9462010	Stevenson	Oct 2016	B1
9467478	Khan et al.	Oct 2016	B1
9485163	Fries et al.	Nov 2016	B1
9521067	Michael et al.	Dec 2016	B2
9525564	Lee	Dec 2016	B2
9542219	Bryant et al.	Jan 2017	B1
9559951	Sajassi et al.	Jan 2017	B1
9563423	Pittman	Feb 2017	B1
9602389	Maveli et al.	Mar 2017	B1
9608917	Anderson et al.	Mar 2017	B1
9608962	Chang	Mar 2017	B1
9614748	Battersby et al.	Apr 2017	B1
9621460	Mehta et al.	Apr 2017	B2
9641551	Kariyanahalli	May 2017	B1
9648547	Hart et al.	May 2017	B1
9665432	Kruse et al.	May 2017	B2
9686127	Ramachandran et al.	Jun 2017	B2
9715401	Devine et al.	Jul 2017	B2
9717021	Hughes et al.	Jul 2017	B2
9722815	Mukundan et al.	Aug 2017	B2
9747249	Cherian et al.	Aug 2017	B2
9755965	Yadav et al.	Sep 2017	B1
9787559	Schroeder	Oct 2017	B1
9807004	Koley et al.	Oct 2017	B2
9819540	Bahadur et al.	Nov 2017	B1
9819565	Djukic et al.	Nov 2017	B2
9825822	Holland	Nov 2017	B1
9825911	Brandwine	Nov 2017	B1
9825992	Xu	Nov 2017	B2
9832128	Ashner et al.	Nov 2017	B1
9832205	Santhi et al.	Nov 2017	B2
9875355	Williams	Jan 2018	B1
9906401	Rao	Feb 2018	B1
9923826	Murgia	Mar 2018	B2
9930011	Clemons, Jr. et al.	Mar 2018	B1
9935829	Miller et al.	Apr 2018	B1
9942787	Tillotson	Apr 2018	B1
9996370	Khafizov et al.	Jun 2018	B1
10038601	Becker et al.	Jul 2018	B1
10057183	Salle et al.	Aug 2018	B2
10057294	Xu	Aug 2018	B2
10116593	Sinn et al.	Oct 2018	B1
10135789	Mayya et al.	Nov 2018	B2
10142226	Wu et al.	Nov 2018	B1
10178032	Freitas	Jan 2019	B1
10178037	Appleby et al.	Jan 2019	B2
10187289	Chen et al.	Jan 2019	B1
10200264	Menon et al.	Feb 2019	B2
10229017	Zou et al.	Mar 2019	B1
10237123	Dubey et al.	Mar 2019	B2
10250498	Bales et al.	Apr 2019	B1
10263832	Ghosh	Apr 2019	B1
10320664	Nainar et al.	Jun 2019	B2
10320691	Matthews et al.	Jun 2019	B1
10326830	Singh	Jun 2019	B1
10348767	Lee et al.	Jul 2019	B1
10355989	Panchal et al.	Jul 2019	B1
10425382	Mayya et al.	Sep 2019	B2
10454708	Mibu	Oct 2019	B2
10454714	Mayya et al.	Oct 2019	B2
10461993	Turabi et al.	Oct 2019	B2
10498652	Mayya et al.	Dec 2019	B2
10511546	Singarayan et al.	Dec 2019	B2
10523539	Mayya et al.	Dec 2019	B2
10550093	Ojima et al.	Feb 2020	B2
10554538	Spohn et al.	Feb 2020	B2
10560431	Chen et al.	Feb 2020	B1
10565464	Han et al.	Feb 2020	B2
10567519	Mukhopadhyaya et al.	Feb 2020	B1
10574482	Oréet al.	Feb 2020	B2
10574528	Mayya et al.	Feb 2020	B2
10594516	Cidon et al.	Mar 2020	B2
10594591	Houjyo et al.	Mar 2020	B2
10594659	El-Moussa et al.	Mar 2020	B2
10608844	Cidon et al.	Mar 2020	B2
10630505	Rubenstein et al.	Apr 2020	B2
10637889	Ermagan et al.	Apr 2020	B2
10666460	Cidon et al.	May 2020	B2
10666497	Tahhan et al.	May 2020	B2
10686625	Cidon et al.	Jun 2020	B2
10693739	Naseri et al.	Jun 2020	B1
10708144	Mohan et al.	Jul 2020	B2
10715427	Raj et al.	Jul 2020	B2
10749711	Mukundan et al.	Aug 2020	B2
10778466	Cidon et al.	Sep 2020	B2
10778528	Mayya et al.	Sep 2020	B2
10778557	Ganichev et al.	Sep 2020	B2
10805114	Cidon et al.	Oct 2020	B2
10805272	Mayya et al.	Oct 2020	B2
10819564	Turabi et al.	Oct 2020	B2
10826775	Moreno et al.	Nov 2020	B1
10841131	Cidon et al.	Nov 2020	B2
10911374	Kumar et al.	Feb 2021	B1
10938693	Mayya et al.	Mar 2021	B2
10951529	Duan et al.	Mar 2021	B2
10958479	Cidon et al.	Mar 2021	B2
10959098	Cidon et al.	Mar 2021	B2
10992558	Silva et al.	Apr 2021	B1
10992568	Michael et al.	Apr 2021	B2
10999100	Cidon et al.	May 2021	B2
10999137	Cidon et al.	May 2021	B2
10999165	Cidon et al.	May 2021	B2
10999197	Hooda et al.	May 2021	B2
11005684	Cidon	May 2021	B2
11018995	Cidon et al.	May 2021	B2
11044190	Ramaswamy et al.	Jun 2021	B2
11050588	Mayya et al.	Jun 2021	B2
11050644	Hegde et al.	Jun 2021	B2
11071005	Shen et al.	Jul 2021	B2
11089111	Markuze et al.	Aug 2021	B2
11095612	Oswal et al.	Aug 2021	B1
11102032	Cidon et al.	Aug 2021	B2
11108595	Knutsen et al.	Aug 2021	B2
11108851	Kurmala et al.	Aug 2021	B1
11115347	Gupta et al.	Sep 2021	B2
11115426	Pazhyannur et al.	Sep 2021	B1
11115480	Markuze et al.	Sep 2021	B2
11121962	Michael et al.	Sep 2021	B2
11121985	Cidon et al.	Sep 2021	B2
11128492	Sethi et al.	Sep 2021	B2
11146632	Rubenstein	Oct 2021	B2
11153230	Cidon et al.	Oct 2021	B2
11171885	Cidon et al.	Nov 2021	B2
11212140	Mukundan et al.	Dec 2021	B2
11212238	Cidon et al.	Dec 2021	B2
11223514	Mayya et al.	Jan 2022	B2
11245641	Ramaswamy et al.	Feb 2022	B2
11252079	Michael et al.	Feb 2022	B2
11252105	Cidon et al.	Feb 2022	B2
11252106	Cidon et al.	Feb 2022	B2
11258728	Cidon et al.	Feb 2022	B2
11310170	Cidon et al.	Apr 2022	B2
11323307	Mayya et al.	May 2022	B2
11349722	Mayya et al.	May 2022	B2
11363124	Markuze et al.	Jun 2022	B2
11374904	Mayya et al.	Jun 2022	B2
11375005	Rolando et al.	Jun 2022	B1
11381474	Kumar et al.	Jul 2022	B1
11381499	Ramaswamy et al.	Jul 2022	B1
11388086	Ramaswamy et al.	Jul 2022	B1
11394640	Ramaswamy et al.	Jul 2022	B2
11418997	Devadoss et al.	Aug 2022	B2
11438789	Devadoss et al.	Sep 2022	B2
11444865	Ramaswamy et al.	Sep 2022	B2
11444872	Mayya et al.	Sep 2022	B2
11477127	Ramaswamy et al.	Oct 2022	B2
11489720	Kempanna et al.	Nov 2022	B1
11489783	Ramaswamy et al.	Nov 2022	B2
11509571	Ramaswamy et al.	Nov 2022	B1
11516049	Cidon et al.	Nov 2022	B2
11522780	Wallace et al.	Dec 2022	B1
11526434	Brooker et al.	Dec 2022	B1
11533248	Mayya et al.	Dec 2022	B2
11552874	Pragada et al.	Jan 2023	B1
11575591	Ramaswamy et al.	Feb 2023	B2
11575600	Markuze et al.	Feb 2023	B2
11582144	Ramaswamy et al.	Feb 2023	B2
11582298	Hood et al.	Feb 2023	B2
11601356	Gandhi et al.	Mar 2023	B2
11606225	Cidon et al.	Mar 2023	B2
11606286	Michael et al.	Mar 2023	B2
11606314	Cidon et al.	Mar 2023	B2
11606712	Devadoss et al.	Mar 2023	B2
11611507	Ramaswamy et al.	Mar 2023	B2
11637768	Ramaswamy et al.	Apr 2023	B2
11677720	Mayya et al.	Jun 2023	B2
11689959	Devadoss et al.	Jun 2023	B2
11700196	Michael et al.	Jul 2023	B2
20020049687	Delsper et al.	Apr 2002	A1
20020075542	Kumar et al.	Jun 2002	A1
20020085488	Kobayashi	Jul 2002	A1
20020087716	Mustafa	Jul 2002	A1
20020152306	Tuck	Oct 2002	A1
20020186682	Kawano et al.	Dec 2002	A1
20020198840	Banka et al.	Dec 2002	A1
20030050061	Wu et al.	Mar 2003	A1
20030061269	Hathaway et al.	Mar 2003	A1
20030088697	Matsuhira	May 2003	A1
20030112766	Riedel et al.	Jun 2003	A1
20030112808	Solomon	Jun 2003	A1
20030126468	Markham	Jul 2003	A1
20030161313	Jinmei et al.	Aug 2003	A1
20030189919	Gupta et al.	Oct 2003	A1
20030202506	Perkins et al.	Oct 2003	A1
20030219030	Gubbi	Nov 2003	A1
20040059831	Chu et al.	Mar 2004	A1
20040068668	Lor et al.	Apr 2004	A1
20040165601	Liu et al.	Aug 2004	A1
20040224771	Chen et al.	Nov 2004	A1
20050078690	DeLangis	Apr 2005	A1
20050149604	Navada	Jul 2005	A1
20050154790	Nagata et al.	Jul 2005	A1
20050172161	Cruz et al.	Aug 2005	A1
20050195754	Nosella	Sep 2005	A1
20050210479	Andjelic	Sep 2005	A1
20050265255	Kodialam et al.	Dec 2005	A1
20060002291	Alicherry et al.	Jan 2006	A1
20060034335	Karaoguz et al.	Feb 2006	A1
20060114838	Mandavilli et al.	Jun 2006	A1
20060171365	Borella	Aug 2006	A1
20060182034	Klinker et al.	Aug 2006	A1
20060182035	Vasseur	Aug 2006	A1
20060193247	Naseh et al.	Aug 2006	A1
20060193252	Naseh et al.	Aug 2006	A1
20060195605	Sundarrajan	Aug 2006	A1
20060245414	Susai	Nov 2006	A1
20070050594	Augsburg et al.	Mar 2007	A1
20070064604	Chen et al.	Mar 2007	A1
20070064702	Bates et al.	Mar 2007	A1
20070083727	Johnston et al.	Apr 2007	A1
20070091794	Filsfils et al.	Apr 2007	A1
20070103548	Carter	May 2007	A1
20070115812	Hughes	May 2007	A1
20070121486	Guichard et al.	May 2007	A1
20070130325	Lesser	Jun 2007	A1
20070162619	Aloni et al.	Jul 2007	A1
20070162639	Chu et al.	Jul 2007	A1
20070177511	Das et al.	Aug 2007	A1
20070195797	Patel et al.	Aug 2007	A1
20070237081	Kodialam et al.	Oct 2007	A1
20070260746	Mirtorabi et al.	Nov 2007	A1
20070268882	Breslau et al.	Nov 2007	A1
20080002670	Bugenhagen et al.	Jan 2008	A1
20080049621	McGuire et al.	Feb 2008	A1
20080055241	Goldenberg et al.	Mar 2008	A1
20080080509	Khanna et al.	Apr 2008	A1
20080095187	Jung et al.	Apr 2008	A1
20080117930	Chakareski et al.	May 2008	A1
20080144532	Chamarajanagar et al.	Jun 2008	A1
20080168086	Miller et al.	Jul 2008	A1
20080175150	Bolt et al.	Jul 2008	A1
20080181116	Kavanaugh et al.	Jul 2008	A1
20080219276	Shah	Sep 2008	A1
20080240121	Xiong et al.	Oct 2008	A1
20080263218	Beerends et al.	Oct 2008	A1
20090013210	McIntosh et al.	Jan 2009	A1
20090028092	Rothschild	Jan 2009	A1
20090125617	Klessig et al.	May 2009	A1
20090141642	Sun	Jun 2009	A1
20090154463	Hines et al.	Jun 2009	A1
20090182874	Morford et al.	Jul 2009	A1
20090247204	Sennett et al.	Oct 2009	A1
20090268605	Campbell et al.	Oct 2009	A1
20090274045	Meier et al.	Nov 2009	A1
20090276657	Wetmore et al.	Nov 2009	A1
20090303880	Maltz et al.	Dec 2009	A1
20100008361	Guichard et al.	Jan 2010	A1
20100017802	Lojewski	Jan 2010	A1
20100046532	Okita	Feb 2010	A1
20100061379	Parandekar et al.	Mar 2010	A1
20100080129	Strahan et al.	Apr 2010	A1
20100088440	Banks et al.	Apr 2010	A1
20100091782	Hiscock	Apr 2010	A1
20100091823	Retana et al.	Apr 2010	A1
20100107162	Edwards et al.	Apr 2010	A1
20100118727	Draves et al.	May 2010	A1
20100118886	Saavedra	May 2010	A1
20100128600	Srinivasmurthy et al.	May 2010	A1
20100165985	Sharma et al.	Jul 2010	A1
20100191884	Holenstein et al.	Jul 2010	A1
20100223621	Joshi et al.	Sep 2010	A1
20100226246	Proulx	Sep 2010	A1
20100290422	Haigh et al.	Nov 2010	A1
20100309841	Conte	Dec 2010	A1
20100309912	Mehta et al.	Dec 2010	A1
20100322255	Hao et al.	Dec 2010	A1
20100332657	Elyashev et al.	Dec 2010	A1
20110001604	Ludlow et al.	Jan 2011	A1
20110007752	Silva et al.	Jan 2011	A1
20110032939	Nozaki et al.	Feb 2011	A1
20110035187	DeJori et al.	Feb 2011	A1
20110040814	Higgins	Feb 2011	A1
20110075674	Li et al.	Mar 2011	A1
20110078783	Duan et al.	Mar 2011	A1
20110107139	Middlecamp et al.	May 2011	A1
20110110370	Moreno et al.	May 2011	A1
20110141877	Xu et al.	Jun 2011	A1
20110142041	Imai	Jun 2011	A1
20110153909	Dong	Jun 2011	A1
20110235509	Szymanski	Sep 2011	A1
20110255397	Kadakia et al.	Oct 2011	A1
20110302663	Prodan et al.	Dec 2011	A1
20120008630	Ould-Brahim	Jan 2012	A1
20120027013	Napierala	Feb 2012	A1
20120039309	Evans et al.	Feb 2012	A1
20120099601	Haddad et al.	Apr 2012	A1
20120136697	Peles et al.	May 2012	A1
20120140935	Kruglick	Jun 2012	A1
20120157068	Eichen et al.	Jun 2012	A1
20120173694	Yan et al.	Jul 2012	A1
20120173919	Patel et al.	Jul 2012	A1
20120182940	Taleb et al.	Jul 2012	A1
20120221955	Raleigh et al.	Aug 2012	A1
20120227093	Shatzkamer et al.	Sep 2012	A1
20120240185	Kapoor et al.	Sep 2012	A1
20120250682	Vincent et al.	Oct 2012	A1
20120250686	Vincent et al.	Oct 2012	A1
20120266026	Chikkalingaiah et al.	Oct 2012	A1
20120281706	Agarwal et al.	Nov 2012	A1
20120287818	Corti et al.	Nov 2012	A1
20120300615	Kempf et al.	Nov 2012	A1
20120307659	Yamada	Dec 2012	A1
20120317270	Vrbaski et al.	Dec 2012	A1
20120317291	Wolfe	Dec 2012	A1
20130007505	Spear	Jan 2013	A1
20130019005	Hui et al.	Jan 2013	A1
20130021968	Reznik et al.	Jan 2013	A1
20130044764	Casado et al.	Feb 2013	A1
20130051237	Ong	Feb 2013	A1
20130051399	Zhang et al.	Feb 2013	A1
20130054763	Merwe et al.	Feb 2013	A1
20130086267	Gelenbe et al.	Apr 2013	A1
20130097304	Asthana et al.	Apr 2013	A1
20130103729	Cooney et al.	Apr 2013	A1
20130103834	Dzerve et al.	Apr 2013	A1
20130117530	Kim et al.	May 2013	A1
20130124718	Griffith et al.	May 2013	A1
20130124911	Griffith et al.	May 2013	A1
20130124912	Griffith et al.	May 2013	A1
20130128889	Mathur et al.	May 2013	A1
20130142201	Kim et al.	Jun 2013	A1
20130170354	Takashima et al.	Jul 2013	A1
20130173768	Kundu et al.	Jul 2013	A1
20130173788	Song	Jul 2013	A1
20130182712	Aguayo et al.	Jul 2013	A1
20130185446	Zeng	Jul 2013	A1
20130185729	Vasic et al.	Jul 2013	A1
20130191688	Agarwal et al.	Jul 2013	A1
20130223226	Narayanan et al.	Aug 2013	A1
20130223454	Dunbar et al.	Aug 2013	A1
20130235870	Tripathi et al.	Sep 2013	A1
20130238782	Zhao et al.	Sep 2013	A1
20130242718	Zhang	Sep 2013	A1
20130254599	Katkar et al.	Sep 2013	A1
20130258839	Wang et al.	Oct 2013	A1
20130258847	Zhang et al.	Oct 2013	A1
20130266015	Qu et al.	Oct 2013	A1
20130266019	Qu et al.	Oct 2013	A1
20130283364	Chang et al.	Oct 2013	A1
20130286846	Atlas et al.	Oct 2013	A1
20130297611	Moritz et al.	Nov 2013	A1
20130297770	Zhang	Nov 2013	A1
20130301469	Suga	Nov 2013	A1
20130301642	Radhakrishnan et al.	Nov 2013	A1
20130308444	Sem-Jacobsen et al.	Nov 2013	A1
20130315242	Wang et al.	Nov 2013	A1
20130315243	Huang et al.	Nov 2013	A1
20130329548	Nakil et al.	Dec 2013	A1
20130329601	Yin et al.	Dec 2013	A1
20130329734	Chesla et al.	Dec 2013	A1
20130346470	Obstfeld et al.	Dec 2013	A1
20140016464	Shirazipour et al.	Jan 2014	A1
20140019604	Twitchell, Jr.	Jan 2014	A1
20140019750	Dodgson et al.	Jan 2014	A1
20140040975	Raleigh et al.	Feb 2014	A1
20140064283	Balus et al.	Mar 2014	A1
20140071832	Johnsson et al.	Mar 2014	A1
20140092907	Sridhar et al.	Apr 2014	A1
20140108665	Arora et al.	Apr 2014	A1
20140112171	Pasdar	Apr 2014	A1
20140115584	Mudigonda et al.	Apr 2014	A1
20140122559	Branson et al.	May 2014	A1
20140123135	Huang et al.	May 2014	A1
20140126418	Brendel et al.	May 2014	A1
20140156818	Hunt	Jun 2014	A1
20140156823	Liu et al.	Jun 2014	A1
20140157363	Banerjee	Jun 2014	A1
20140160935	Zecharia et al.	Jun 2014	A1
20140164560	Ko et al.	Jun 2014	A1
20140164617	Jalan et al.	Jun 2014	A1
20140164718	Schaik et al.	Jun 2014	A1
20140173113	Vemuri et al.	Jun 2014	A1
20140173331	Martin et al.	Jun 2014	A1
20140181824	Saund et al.	Jun 2014	A1
20140189074	Parker	Jul 2014	A1
20140208317	Nakagawa	Jul 2014	A1
20140219135	Li et al.	Aug 2014	A1
20140223507	Xu	Aug 2014	A1
20140226664	Chen	Aug 2014	A1
20140229210	Sharifian et al.	Aug 2014	A1
20140244851	Lee	Aug 2014	A1
20140258535	Zhang	Sep 2014	A1
20140269690	Tu	Sep 2014	A1
20140279862	Dietz et al.	Sep 2014	A1
20140280499	Basavaiah et al.	Sep 2014	A1
20140310282	Sprague et al.	Oct 2014	A1
20140317440	Biermayr et al.	Oct 2014	A1
20140321277	Lynn, Jr. et al.	Oct 2014	A1
20140337500	Lee	Nov 2014	A1
20140337674	Ivancic et al.	Nov 2014	A1
20140341109	Cartmell et al.	Nov 2014	A1
20140355441	Jain	Dec 2014	A1
20140365834	Stone et al.	Dec 2014	A1
20140372582	Ghanwani et al.	Dec 2014	A1
20150003240	Drwiega et al.	Jan 2015	A1
20150016249	Mukundan et al.	Jan 2015	A1
20150029864	Raileanu et al.	Jan 2015	A1
20150039744	Niazi et al.	Feb 2015	A1
20150046572	Cheng et al.	Feb 2015	A1
20150052247	Threefoot et al.	Feb 2015	A1
20150052517	Raghu et al.	Feb 2015	A1
20150056960	Egner et al.	Feb 2015	A1
20150058917	Xu	Feb 2015	A1
20150088942	Shah	Mar 2015	A1
20150089628	Lang	Mar 2015	A1
20150092603	Aguayo et al.	Apr 2015	A1
20150096011	Watt	Apr 2015	A1
20150100958	Banavalikar et al.	Apr 2015	A1
20150106809	Reddy et al.	Apr 2015	A1
20150124603	Ketheesan et al.	May 2015	A1
20150134777	Onoue	May 2015	A1
20150139238	Pourzandi et al.	May 2015	A1
20150146539	Mehta et al.	May 2015	A1
20150163152	Li	Jun 2015	A1
20150169340	Haddad et al.	Jun 2015	A1
20150172121	Farkas et al.	Jun 2015	A1
20150172169	DeCusatis et al.	Jun 2015	A1
20150188823	Williams et al.	Jul 2015	A1
20150189009	Bemmel	Jul 2015	A1
20150195178	Bhattacharya et al.	Jul 2015	A1
20150201036	Nishiki et al.	Jul 2015	A1
20150222543	Song	Aug 2015	A1
20150222638	Morley	Aug 2015	A1
20150236945	Michael et al.	Aug 2015	A1
20150236962	Veres et al.	Aug 2015	A1
20150244617	Nakil et al.	Aug 2015	A1
20150249644	Xu	Sep 2015	A1
20150257081	Ramanujan et al.	Sep 2015	A1
20150264055	Budhani	Sep 2015	A1
20150271056	Chunduri et al.	Sep 2015	A1
20150271104	Chikkamath et al.	Sep 2015	A1
20150271303	Neginhal et al.	Sep 2015	A1
20150281004	Kakadia et al.	Oct 2015	A1
20150312142	Barabash et al.	Oct 2015	A1
20150312760	O'Toole	Oct 2015	A1
20150317169	Sinha et al.	Nov 2015	A1
20150326426	Luo et al.	Nov 2015	A1
20150334025	Rader	Nov 2015	A1
20150334696	Gu et al.	Nov 2015	A1
20150341271	Gomez	Nov 2015	A1
20150349978	Wu et al.	Dec 2015	A1
20150350907	Timariu et al.	Dec 2015	A1
20150358232	Chen et al.	Dec 2015	A1
20150358236	Roach et al.	Dec 2015	A1
20150363221	Terayama et al.	Dec 2015	A1
20150363733	Brown	Dec 2015	A1
20150365323	Duminuco et al.	Dec 2015	A1
20150372943	Hasan et al.	Dec 2015	A1
20150372982	Herle et al.	Dec 2015	A1
20150381407	Wang et al.	Dec 2015	A1
20150381493	Bansal et al.	Dec 2015	A1
20160019317	Pawar et al.	Jan 2016	A1
20160020844	Hart et al.	Jan 2016	A1
20160021597	Hart et al.	Jan 2016	A1
20160035183	Buchholz et al.	Feb 2016	A1
20160036924	Koppolu et al.	Feb 2016	A1
20160036938	Aviles et al.	Feb 2016	A1
20160037434	Gopal et al.	Feb 2016	A1
20160072669	Saavedra	Mar 2016	A1
20160072684	Manuguri et al.	Mar 2016	A1
20160080268	Anand et al.	Mar 2016	A1
20160080502	Yadav et al.	Mar 2016	A1
20160105353	Cociglio	Apr 2016	A1
20160105392	Thakkar et al.	Apr 2016	A1
20160105471	Nunes et al.	Apr 2016	A1
20160105488	Thakkar et al.	Apr 2016	A1
20160117185	Fang et al.	Apr 2016	A1
20160134461	Sampath et al.	May 2016	A1
20160134527	Kwak et al.	May 2016	A1
20160134528	Lin et al.	May 2016	A1
20160134591	Liao et al.	May 2016	A1
20160142373	Ossipov	May 2016	A1
20160147607	Dornemann et al.	May 2016	A1
20160150055	Choi	May 2016	A1
20160164832	Bellagamba et al.	Jun 2016	A1
20160164914	Madhav et al.	Jun 2016	A1
20160173338	Wolting	Jun 2016	A1
20160191363	Haraszti et al.	Jun 2016	A1
20160191374	Singh et al.	Jun 2016	A1
20160192403	Gupta et al.	Jun 2016	A1
20160197834	Luft	Jul 2016	A1
20160197835	Luft	Jul 2016	A1
20160198003	Luft	Jul 2016	A1
20160205071	Cooper et al.	Jul 2016	A1
20160210209	Verkaik et al.	Jul 2016	A1
20160212773	Kanderholm et al.	Jul 2016	A1
20160218947	Hughes et al.	Jul 2016	A1
20160218951	Vasseur et al.	Jul 2016	A1
20160234099	Jiao	Aug 2016	A1
20160234161	Banerjee et al.	Aug 2016	A1
20160255169	Kovvuri et al.	Sep 2016	A1
20160255542	Hughes et al.	Sep 2016	A1
20160261493	Li	Sep 2016	A1
20160261495	Xia et al.	Sep 2016	A1
20160261506	Hegde et al.	Sep 2016	A1
20160261639	Xu	Sep 2016	A1
20160269298	Li et al.	Sep 2016	A1
20160269926	Sundaram	Sep 2016	A1
20160285736	Gu	Sep 2016	A1
20160299775	Madapurath et al.	Oct 2016	A1
20160301471	Kunz et al.	Oct 2016	A1
20160308762	Teng et al.	Oct 2016	A1
20160315912	Mayya et al.	Oct 2016	A1
20160323377	Einkauf et al.	Nov 2016	A1
20160328159	Coddington et al.	Nov 2016	A1
20160330111	Manghirmalani et al.	Nov 2016	A1
20160337202	Ben-Itzhak et al.	Nov 2016	A1
20160352588	Subbarayan et al.	Dec 2016	A1
20160353268	Senarath et al.	Dec 2016	A1
20160359738	Sullenberger et al.	Dec 2016	A1
20160366187	Kamble	Dec 2016	A1
20160371153	Dornemann	Dec 2016	A1
20160378527	Zamir	Dec 2016	A1
20160380886	Blair et al.	Dec 2016	A1
20160380906	Hodique et al.	Dec 2016	A1
20170005986	Bansal et al.	Jan 2017	A1
20170006499	Hampel et al.	Jan 2017	A1
20170012870	Blair et al.	Jan 2017	A1
20170019428	Cohn	Jan 2017	A1
20170026273	Yao et al.	Jan 2017	A1
20170026283	Williams et al.	Jan 2017	A1
20170026355	Mathaiyan et al.	Jan 2017	A1
20170034046	Cai et al.	Feb 2017	A1
20170034052	Chanda et al.	Feb 2017	A1
20170034129	Sawant et al.	Feb 2017	A1
20170048296	Ramalho et al.	Feb 2017	A1
20170053258	Carney et al.	Feb 2017	A1
20170055131	Kong et al.	Feb 2017	A1
20170063674	Maskalik et al.	Mar 2017	A1
20170063782	Jain et al.	Mar 2017	A1
20170063783	Yong et al.	Mar 2017	A1
20170063794	Jain et al.	Mar 2017	A1
20170064005	Lee	Mar 2017	A1
20170075710	Prasad et al.	Mar 2017	A1
20170093625	Pera et al.	Mar 2017	A1
20170097841	Chang et al.	Apr 2017	A1
20170104653	Badea et al.	Apr 2017	A1
20170104755	Arregoces et al.	Apr 2017	A1
20170109212	Gaurav et al.	Apr 2017	A1
20170118067	Vedula	Apr 2017	A1
20170118173	Arramreddy et al.	Apr 2017	A1
20170123939	Maheshwari et al.	May 2017	A1
20170126475	Mahkonen et al.	May 2017	A1
20170126516	Tiagi et al.	May 2017	A1
20170126564	Mayya et al.	May 2017	A1
20170134186	Mukundan et al.	May 2017	A1
20170134520	Abbasi et al.	May 2017	A1
20170139789	Fries et al.	May 2017	A1
20170142000	Cai et al.	May 2017	A1
20170149637	Banikazemi et al.	May 2017	A1
20170155557	Desai et al.	Jun 2017	A1
20170155566	Martinsen et al.	Jun 2017	A1
20170155590	Dillon et al.	Jun 2017	A1
20170163473	Sadana et al.	Jun 2017	A1
20170171024	Anerousis et al.	Jun 2017	A1
20170171310	Gardner	Jun 2017	A1
20170180220	Leckey et al.	Jun 2017	A1
20170181210	Nadella et al.	Jun 2017	A1
20170195161	Ruel et al.	Jul 2017	A1
20170195169	Mills et al.	Jul 2017	A1
20170201568	Hussam et al.	Jul 2017	A1
20170201585	Doraiswamy et al.	Jul 2017	A1
20170207976	Rovner et al.	Jul 2017	A1
20170214545	Cheng et al.	Jul 2017	A1
20170214701	Hasan	Jul 2017	A1
20170223117	Messerli et al.	Aug 2017	A1
20170236060	Gnatyev	Aug 2017	A1
20170237710	Mayya et al.	Aug 2017	A1
20170242784	Heorhiadi et al.	Aug 2017	A1
20170257260	Govindan et al.	Sep 2017	A1
20170257309	Appanna	Sep 2017	A1
20170264496	Ao et al.	Sep 2017	A1
20170279717	Bethers et al.	Sep 2017	A1
20170279741	Elias et al.	Sep 2017	A1
20170279803	Desai et al.	Sep 2017	A1
20170280474	Vesterinen et al.	Sep 2017	A1
20170288987	Pasupathy et al.	Oct 2017	A1
20170289002	Ganguli et al.	Oct 2017	A1
20170289027	Ratnasingham	Oct 2017	A1
20170295264	Touitou et al.	Oct 2017	A1
20170302501	Shi et al.	Oct 2017	A1
20170302565	Ghobadi et al.	Oct 2017	A1
20170310641	Jiang et al.	Oct 2017	A1
20170310691	Vasseur et al.	Oct 2017	A1
20170317954	Masurekar et al.	Nov 2017	A1
20170317969	Masurekar et al.	Nov 2017	A1
20170317974	Masurekar et al.	Nov 2017	A1
20170324628	Dhanabalan	Nov 2017	A1
20170337086	Zhu et al.	Nov 2017	A1
20170339022	Hegde et al.	Nov 2017	A1
20170339054	Yadav et al.	Nov 2017	A1
20170339070	Chang et al.	Nov 2017	A1
20170346722	Smith et al.	Nov 2017	A1
20170364419	Lo	Dec 2017	A1
20170366445	Nemirovsky et al.	Dec 2017	A1
20170366467	Martin et al.	Dec 2017	A1
20170373950	Szilagyi et al.	Dec 2017	A1
20170374174	Evens et al.	Dec 2017	A1
20180006995	Bickhart et al.	Jan 2018	A1
20180007005	Chanda et al.	Jan 2018	A1
20180007123	Cheng et al.	Jan 2018	A1
20180013636	Seetharamaiah et al.	Jan 2018	A1
20180014051	Phillips et al.	Jan 2018	A1
20180020035	Boggia et al.	Jan 2018	A1
20180034668	Mayya et al.	Feb 2018	A1
20180041425	Zhang	Feb 2018	A1
20180062875	Tumuluru	Mar 2018	A1
20180062914	Boutros et al.	Mar 2018	A1
20180062917	Chandrashekhar et al.	Mar 2018	A1
20180063036	Chandrashekhar et al.	Mar 2018	A1
20180063193	Chandrashekhar et al.	Mar 2018	A1
20180063233	Park	Mar 2018	A1
20180063743	Tumuluru et al.	Mar 2018	A1
20180069924	Tumuluru et al.	Mar 2018	A1
20180074909	Bishop et al.	Mar 2018	A1
20180077081	Lauer et al.	Mar 2018	A1
20180077202	Xu	Mar 2018	A1
20180084081	Kuchibhotla et al.	Mar 2018	A1
20180091370	Arai	Mar 2018	A1
20180097725	Wood et al.	Apr 2018	A1
20180114569	Strachan et al.	Apr 2018	A1
20180123910	Fitzgibbon	May 2018	A1
20180123946	Ramachandran et al.	May 2018	A1
20180131608	Jiang et al.	May 2018	A1
20180131615	Zhang	May 2018	A1
20180131720	Hobson et al.	May 2018	A1
20180145899	Rao	May 2018	A1
20180159796	Wang et al.	Jun 2018	A1
20180159856	Gujarathi	Jun 2018	A1
20180167378	Kostyukov et al.	Jun 2018	A1
20180176073	Dubey et al.	Jun 2018	A1
20180176082	Katz et al.	Jun 2018	A1
20180176130	Banerjee et al.	Jun 2018	A1
20180176252	Nimmagadda et al.	Jun 2018	A1
20180181423	Gunda et al.	Jun 2018	A1
20180205746	Boutnaru et al.	Jul 2018	A1
20180213472	Ishii et al.	Jul 2018	A1
20180219765	Michael et al.	Aug 2018	A1
20180219766	Michael et al.	Aug 2018	A1
20180234300	Mayya et al.	Aug 2018	A1
20180248790	Tan et al.	Aug 2018	A1
20180260125	Botes et al.	Sep 2018	A1
20180261085	Liu et al.	Sep 2018	A1
20180262468	Kumar et al.	Sep 2018	A1
20180270104	Zheng et al.	Sep 2018	A1
20180278541	Wu et al.	Sep 2018	A1
20180287907	Kulshreshtha et al.	Oct 2018	A1
20180295101	Gehrmann	Oct 2018	A1
20180295529	Jen et al.	Oct 2018	A1
20180302286	Mayya et al.	Oct 2018	A1
20180302321	Manthiramoorthy et al.	Oct 2018	A1
20180307851	Lewis	Oct 2018	A1
20180316606	Sung et al.	Nov 2018	A1
20180351855	Sood et al.	Dec 2018	A1
20180351862	Jeganathan et al.	Dec 2018	A1
20180351863	Vairavakkalai et al.	Dec 2018	A1
20180351882	Jeganathan et al.	Dec 2018	A1
20180359323	Madden	Dec 2018	A1
20180367445	Bajaj	Dec 2018	A1
20180373558	Chang et al.	Dec 2018	A1
20180375744	Mayya et al.	Dec 2018	A1
20180375824	Mayya et al.	Dec 2018	A1
20180375967	Pithawala et al.	Dec 2018	A1
20190013883	Vargas et al.	Jan 2019	A1
20190014038	Ritchie	Jan 2019	A1
20190020588	Twitchell, Jr.	Jan 2019	A1
20190020627	Yuan	Jan 2019	A1
20190021085	Mochizuki et al.	Jan 2019	A1
20190028378	Houjyo et al.	Jan 2019	A1
20190028552	Johnson et al.	Jan 2019	A1
20190036808	Shenoy et al.	Jan 2019	A1
20190036810	Michael et al.	Jan 2019	A1
20190036813	Shenoy et al.	Jan 2019	A1
20190046056	Khachaturian et al.	Feb 2019	A1
20190058657	Chunduri et al.	Feb 2019	A1
20190058709	Kempf et al.	Feb 2019	A1
20190068470	Mirsky	Feb 2019	A1
20190068493	Ram et al.	Feb 2019	A1
20190068500	Hira	Feb 2019	A1
20190075083	Mayya et al.	Mar 2019	A1
20190081894	Yousaf et al.	Mar 2019	A1
20190103990	Cidon et al.	Apr 2019	A1
20190103991	Cidon et al.	Apr 2019	A1
20190103992	Cidon et al.	Apr 2019	A1
20190103993	Cidon et al.	Apr 2019	A1
20190104035	Cidon et al.	Apr 2019	A1
20190104049	Cidon et al.	Apr 2019	A1
20190104050	Cidon et al.	Apr 2019	A1
20190104051	Cidon et al.	Apr 2019	A1
20190104052	Cidon et al.	Apr 2019	A1
20190104053	Cidon et al.	Apr 2019	A1
20190104063	Cidon et al.	Apr 2019	A1
20190104064	Cidon et al.	Apr 2019	A1
20190104109	Cidon et al.	Apr 2019	A1
20190104111	Cidon et al.	Apr 2019	A1
20190104413	Cidon et al.	Apr 2019	A1
20190109769	Jain et al.	Apr 2019	A1
20190132221	Boutros et al.	May 2019	A1
20190132234	Dong et al.	May 2019	A1
20190132322	Song et al.	May 2019	A1
20190140889	Mayya et al.	May 2019	A1
20190140890	Mayya et al.	May 2019	A1
20190149525	Gunda et al.	May 2019	A1
20190158371	Dillon et al.	May 2019	A1
20190158605	Markuze et al.	May 2019	A1
20190199539	Deng et al.	Jun 2019	A1
20190220703	Prakash et al.	Jul 2019	A1
20190222499	Chen et al.	Jul 2019	A1
20190238364	Boutros et al.	Aug 2019	A1
20190238446	Barzik et al.	Aug 2019	A1
20190238449	Michael et al.	Aug 2019	A1
20190238450	Michael et al.	Aug 2019	A1
20190238483	Marichetty et al.	Aug 2019	A1
20190238497	Tourrilhes et al.	Aug 2019	A1
20190268421	Markuze et al.	Aug 2019	A1
20190268973	Bull et al.	Aug 2019	A1
20190278631	Bernat et al.	Sep 2019	A1
20190280962	Michael et al.	Sep 2019	A1
20190280963	Michael et al.	Sep 2019	A1
20190280964	Michael et al.	Sep 2019	A1
20190288875	Shen et al.	Sep 2019	A1
20190306197	Degioanni	Oct 2019	A1
20190306282	Masputra et al.	Oct 2019	A1
20190313278	Liu	Oct 2019	A1
20190313907	Khachaturian et al.	Oct 2019	A1
20190319847	Nahar et al.	Oct 2019	A1
20190319881	Maskara et al.	Oct 2019	A1
20190327109	Guichard et al.	Oct 2019	A1
20190334786	Dutta et al.	Oct 2019	A1
20190334813	Raj et al.	Oct 2019	A1
20190334820	Zhao	Oct 2019	A1
20190342201	Singh	Nov 2019	A1
20190342219	Liu et al.	Nov 2019	A1
20190356736	Narayanaswamy et al.	Nov 2019	A1
20190364099	Thakkar et al.	Nov 2019	A1
20190364456	Yu	Nov 2019	A1
20190372888	Michael et al.	Dec 2019	A1
20190372889	Michael et al.	Dec 2019	A1
20190372890	Michael et al.	Dec 2019	A1
20190394081	Tahhan et al.	Dec 2019	A1
20200014609	Tockett et al.	Jan 2020	A1
20200014615	Michael et al.	Jan 2020	A1
20200014616	Michael et al.	Jan 2020	A1
20200014661	Mayya et al.	Jan 2020	A1
20200014663	Chen et al.	Jan 2020	A1
20200021514	Michael et al.	Jan 2020	A1
20200021515	Michael et al.	Jan 2020	A1
20200036624	Michael et al.	Jan 2020	A1
20200044943	Bor-Yaliniz et al.	Feb 2020	A1
20200044969	Hao et al.	Feb 2020	A1
20200059420	Abraham	Feb 2020	A1
20200059457	Raza et al.	Feb 2020	A1
20200059459	Abraham et al.	Feb 2020	A1
20200067831	Spraggins et al.	Feb 2020	A1
20200092207	Sipra et al.	Mar 2020	A1
20200097327	Beyer et al.	Mar 2020	A1
20200099625	Yigit et al.	Mar 2020	A1
20200099659	Cometto et al.	Mar 2020	A1
20200106696	Michael et al.	Apr 2020	A1
20200106706	Mayya et al.	Apr 2020	A1
20200119952	Mayya et al.	Apr 2020	A1
20200127905	Mayya et al.	Apr 2020	A1
20200127911	Gilson et al.	Apr 2020	A1
20200153701	Mohan et al.	May 2020	A1
20200153736	Liebherr et al.	May 2020	A1
20200159661	Keymolen et al.	May 2020	A1
20200162407	Tillotson	May 2020	A1
20200169473	Rimar et al.	May 2020	A1
20200177503	Hooda et al.	Jun 2020	A1
20200177550	Valluri et al.	Jun 2020	A1
20200177629	Hooda et al.	Jun 2020	A1
20200186471	Shen et al.	Jun 2020	A1
20200195557	Duan et al.	Jun 2020	A1
20200204460	Schneider et al.	Jun 2020	A1
20200213212	Dillon et al.	Jul 2020	A1
20200213224	Cheng et al.	Jul 2020	A1
20200218558	Sreenath et al.	Jul 2020	A1
20200235990	Janakiraman et al.	Jul 2020	A1
20200235999	Mayya et al.	Jul 2020	A1
20200236046	Jain et al.	Jul 2020	A1
20200241927	Yang et al.	Jul 2020	A1
20200244721	S et al.	Jul 2020	A1
20200252234	Ramamoorthi et al.	Aug 2020	A1
20200259700	Bhalla et al.	Aug 2020	A1
20200267184	Vera-Schockner	Aug 2020	A1
20200267203	Jindal et al.	Aug 2020	A1
20200280587	Janakiraman et al.	Sep 2020	A1
20200287819	Theogaraj et al.	Sep 2020	A1
20200287976	Theogaraj et al.	Sep 2020	A1
20200296011	Jain et al.	Sep 2020	A1
20200296026	Michael et al.	Sep 2020	A1
20200301764	Thoresen et al.	Sep 2020	A1
20200314006	Mackie et al.	Oct 2020	A1
20200314614	Moustafa et al.	Oct 2020	A1
20200322230	Natal et al.	Oct 2020	A1
20200322287	Connor et al.	Oct 2020	A1
20200336336	Sethi et al.	Oct 2020	A1
20200344089	Motwani et al.	Oct 2020	A1
20200344143	Faseela et al.	Oct 2020	A1
20200344163	Gupta et al.	Oct 2020	A1
20200351188	Arora et al.	Nov 2020	A1
20200358878	Bansal et al.	Nov 2020	A1
20200366530	Mukundan et al.	Nov 2020	A1
20200366562	Mayya et al.	Nov 2020	A1
20200382345	Zhao et al.	Dec 2020	A1
20200382387	Pasupathy et al.	Dec 2020	A1
20200403821	Dev et al.	Dec 2020	A1
20200412483	Tan et al.	Dec 2020	A1
20200412576	Kondapavuluru et al.	Dec 2020	A1
20200413283	Shen et al.	Dec 2020	A1
20210006482	Hwang et al.	Jan 2021	A1
20210006490	Michael et al.	Jan 2021	A1
20210021538	Meck et al.	Jan 2021	A1
20210029019	Kottapalli	Jan 2021	A1
20210029088	Mayya et al.	Jan 2021	A1
20210036888	Makkalla et al.	Feb 2021	A1
20210036987	Mishra et al.	Feb 2021	A1
20210037159	Shimokawa	Feb 2021	A1
20210049191	Masson et al.	Feb 2021	A1
20210067372	Cidon et al.	Mar 2021	A1
20210067373	Cidon et al.	Mar 2021	A1
20210067374	Cidon et al.	Mar 2021	A1
20210067375	Cidon et al.	Mar 2021	A1
20210067407	Cidon et al.	Mar 2021	A1
20210067427	Cidon et al.	Mar 2021	A1
20210067442	Sundararajan et al.	Mar 2021	A1
20210067461	Cidon et al.	Mar 2021	A1
20210067464	Cidon et al.	Mar 2021	A1
20210067467	Cidon et al.	Mar 2021	A1
20210067468	Cidon et al.	Mar 2021	A1
20210073001	Rogers et al.	Mar 2021	A1
20210092062	Dhanabalan et al.	Mar 2021	A1
20210099360	Parsons et al.	Apr 2021	A1
20210105199	H et al.	Apr 2021	A1
20210111998	Saavedra	Apr 2021	A1
20210112034	Sundararajan et al.	Apr 2021	A1
20210126830	R. et al.	Apr 2021	A1
20210126853	Ramaswamy et al.	Apr 2021	A1
20210126854	Guo et al.	Apr 2021	A1
20210126860	Ramaswamy et al.	Apr 2021	A1
20210144091	H et al.	May 2021	A1
20210160169	Shen et al.	May 2021	A1
20210160813	Gupta et al.	May 2021	A1
20210176255	Hill et al.	Jun 2021	A1
20210184952	Mayya et al.	Jun 2021	A1
20210184966	Ramaswamy et al.	Jun 2021	A1
20210184983	Ramaswamy et al.	Jun 2021	A1
20210194814	Roux et al.	Jun 2021	A1
20210226880	Ramamoorthy et al.	Jul 2021	A1
20210234728	Cidon et al.	Jul 2021	A1
20210234775	Devadoss et al.	Jul 2021	A1
20210234786	Devadoss et al.	Jul 2021	A1
20210234804	Devadoss et al.	Jul 2021	A1
20210234805	Devadoss et al.	Jul 2021	A1
20210235312	Devadoss et al.	Jul 2021	A1
20210235313	Devadoss et al.	Jul 2021	A1
20210266262	Subramanian et al.	Aug 2021	A1
20210279069	Salgaonkar et al.	Sep 2021	A1
20210314289	Chandrashekhar et al.	Oct 2021	A1
20210314385	Pande et al.	Oct 2021	A1
20210328835	Mayya et al.	Oct 2021	A1
20210336880	Gupta et al.	Oct 2021	A1
20210377109	Shrivastava et al.	Dec 2021	A1
20210377156	Michael et al.	Dec 2021	A1
20210392060	Silva et al.	Dec 2021	A1
20210392070	Tootaghaj et al.	Dec 2021	A1
20210399920	Sundararajan et al.	Dec 2021	A1
20210399978	Michael et al.	Dec 2021	A9
20210400512	Agarwal et al.	Dec 2021	A1
20210409277	Jeuk et al.	Dec 2021	A1
20220006726	Michael et al.	Jan 2022	A1
20220006751	Ramaswamy et al.	Jan 2022	A1
20220006756	Ramaswamy et al.	Jan 2022	A1
20220029902	Shemer et al.	Jan 2022	A1
20220035673	Markuze et al.	Feb 2022	A1
20220038370	Vasseur et al.	Feb 2022	A1
20220038557	Markuze et al.	Feb 2022	A1
20220045927	Liu et al.	Feb 2022	A1
20220052928	Sundararajan et al.	Feb 2022	A1
20220061059	Dunsmore et al.	Feb 2022	A1
20220086035	Devaraj et al.	Mar 2022	A1
20220094644	Cidon et al.	Mar 2022	A1
20220123961	Mukundan et al.	Apr 2022	A1
20220131740	Mayya et al.	Apr 2022	A1
20220131807	Srinivas et al.	Apr 2022	A1
20220131898	Hooda et al.	Apr 2022	A1
20220141184	Oswal et al.	May 2022	A1
20220158923	Ramaswamy et al.	May 2022	A1
20220158924	Ramaswamy et al.	May 2022	A1
20220158926	Wennerström et al.	May 2022	A1
20220166713	Markuze et al.	May 2022	A1
20220191719	Roy	Jun 2022	A1
20220198229	López et al.	Jun 2022	A1
20220210035	Hendrickson et al.	Jun 2022	A1
20220210041	Gandhi et al.	Jun 2022	A1
20220210042	Gandhi et al.	Jun 2022	A1
20220210122	Levin et al.	Jun 2022	A1
20220217015	Vuggrala et al.	Jul 2022	A1
20220231949	Ramaswamy et al.	Jul 2022	A1
20220231950	Ramaswamy et al.	Jul 2022	A1
20220232411	Vijayakumar et al.	Jul 2022	A1
20220239596	Kumar et al.	Jul 2022	A1
20220294701	Mayya et al.	Sep 2022	A1
20220335027	Seshadri et al.	Oct 2022	A1
20220337553	Mayya et al.	Oct 2022	A1
20220353152	Ramaswamy	Nov 2022	A1
20220353171	Ramaswamy et al.	Nov 2022	A1
20220353175	Ramaswamy et al.	Nov 2022	A1
20220353182	Ramaswamy et al.	Nov 2022	A1
20220353190	Ramaswamy et al.	Nov 2022	A1
20220360500	Ramaswamy et al.	Nov 2022	A1
20220407773	Kempanna et al.	Dec 2022	A1
20220407774	Kempanna et al.	Dec 2022	A1
20220407790	Kempanna et al.	Dec 2022	A1
20220407820	Kempanna et al.	Dec 2022	A1
20220407915	Kempanna et al.	Dec 2022	A1
20230006929	Mayya et al.	Jan 2023	A1
20230025586	Rolando et al.	Jan 2023	A1
20230026330	Rolando et al.	Jan 2023	A1
20230026865	Rolando et al.	Jan 2023	A1
20230028872	Ramaswamy	Jan 2023	A1
20230039869	Ramaswamy et al.	Feb 2023	A1
20230041916	Zhang et al.	Feb 2023	A1
20230054961	Ramaswamy et al.	Feb 2023	A1
20230105680	Simlai et al.	Apr 2023	A1
20230121871	Mayya et al.	Apr 2023	A1
20230179445	Cidon et al.	Jun 2023	A1
20230179502	Ramaswamy et al.	Jun 2023	A1
20230179521	Markuze et al.	Jun 2023	A1
20230179543	Cidon et al.	Jun 2023	A1
20230216768	Zohar et al.	Jul 2023	A1
20230216801	Markuze et al.	Jul 2023	A1
20230216804	Zohar et al.	Jul 2023	A1
20230221874	Markuze et al.	Jul 2023	A1
20230224356	Markuze et al.	Jul 2023	A1
20230224759	Ramaswamy	Jul 2023	A1

Foreign Referenced Citations (52)

Number	Date	Country
1926809	Mar 2007	CN
102577270	Jul 2012	CN
102811165	Dec 2012	CN
104956329	Sep 2015	CN
106230650	Dec 2016	CN
106656847	May 2017	CN
106998284	Aug 2017	CN
110447209	Nov 2019	CN
111198764	May 2020	CN
1912381	Apr 2008	EP
2538637	Dec 2012	EP
2763362	Aug 2014	EP
3041178	Jul 2016	EP
3297211	Mar 2018	EP
3509256	Jul 2019	EP
3346650	Nov 2019	EP
3662619	Jun 2020	EP
3878160	Sep 2021	EP
2002368792	Dec 2002	JP
2010233126	Oct 2010	JP
2014200010	Oct 2014	JP
2017059991	Mar 2017	JP
2017524290	Aug 2017	JP
20170058201	May 2017	KR
2574350	Feb 2016	RU
03073701	Sep 2003	WO
2005071861	Aug 2005	WO
2007016834	Feb 2007	WO
2012167184	Dec 2012	WO
2015092565	Jun 2015	WO
2016061546	Apr 2016	WO
2016123314	Aug 2016	WO
2017083975	May 2017	WO
2019070611	Apr 2019	WO
2019094522	May 2019	WO
2020012491	Jan 2020	WO
2020018704	Jan 2020	WO
2020091777	May 2020	WO
2020101922	May 2020	WO
2020112345	Jun 2020	WO
2021040934	Mar 2021	WO
2021118717	Jun 2021	WO
2021150465	Jul 2021	WO
2021211906	Oct 2021	WO
2022005607	Jan 2022	WO
2022082680	Apr 2022	WO
2022154850	Jul 2022	WO
2022159156	Jul 2022	WO
2022231668	Nov 2022	WO
2022235303	Nov 2022	WO
2022265681	Dec 2022	WO
2023009159	Feb 2023	WO

Non-Patent Literature Citations (66)

Entry
Alvizu, Rodolfo, et al., “SDN-Based Network Orchestration for New Dynamic Enterprise Networking Services,” 2017 19th International Conference on Transparent Optical Networks, Jul. 2-6, 2017, 4 pages, IEEE, Girona, Spain.
Barozet, Jean-Marc, “Cisco SD-WAN as a Managed Service,” BRKRST-2558, Jan. 27-31, 2020, 98 pages, Cisco, Barcelona, Spain, retrieved from https://www.ciscolive.com/c/dam/r/ciscolive/emea/docs/2020/pdf/BRKRST-2558.pdf.
Barozet, Jean-Marc, “Cisco SDWAN,” Deep Dive, Dec. 2017, 185 pages, Cisco, Retreived from https://www.coursehero.com/file/71671376/Cisco-SDWAN-Deep-Divepdf/.
Bertaux, Lionel, et al., “Software Defined Networking and Virtualization for Broadband Satellite Networks,” IEEE Communications Magazine, Mar. 18, 2015, 7 pages, vol. 53, IEEE, retrieved from https://ieeexplore.ieee.org/document/7060482.
Cox, Jacob H., et al., “Advancing Software-Defined Networks: A Survey,” IEEE Access, Oct. 12, 2017, 40 pages, vol. 5, IEEE, retrieved from https://ieeexplore.ieee.org/document/8066287.
Duan, Zhenhai, et al., “Service Overlay Networks: SLAs, QoS, and Bandwidth Provisioning,” IEEE/ACM Transactions on Networking, Dec. 2003, 14 pages, vol. 11, IEEE, New York, NY, USA.
Jivorasetkul, Supalerk, et al., “End-to-End Header Compression over Software-Defined Networks: a Low Latency Network Architecture,” 2012 Fourth International Conference on Intelligent Networking and Collaborative Systems, Sep. 19-21, 2012, 2 pages, IEEE, Bucharest, Romania.
Li, Shengru, et al., “Source Routing with Protocol-oblivious Forwarding (POF) to Enable Efficient e-Health Data Transfers,” 2016 IEEE International Conference on Communications (ICC), May 22-27, 2016, 6 pages, IEEE, Kuala Lumpur, Malaysia.
Ming, Gao, et al., “A Design of SD-WAN-Oriented Wide Area Network Access,” 2020 International Conference on Computer Communication and Network Security (CCNS), Aug. 21-23, 2020, 4 pages, IEEE, Xi'an, China.
Tootaghaj, Diman Zad, et al., “Homa: An Efficient Topology and Route Management Approach in SD-WAN Overlays,” IEEE INFOCOM 2020—IEEE Conference on Computer Communications, Jul. 6-9, 2020, 10 pages, IEEE, Toronto, ON, Canada.
Non-Published Commonly Owned U.S. Appl. No. 17/827,972, filed May 30, 2022, 30 pages, Nicira, Inc.
Non-Published Commonly Owned U.S. Appl. No. 17/850,112, filed Jun. 27, 2022, 41 pages, Nicira, Inc.
Alsaeedi, Mohammed, et al., “Toward Adaptive and Scalable OpenFlow-SDN Flow Control: A Survey,” IEEE Access, Aug. 1, 2019, 34 pages, vol. 7, IEEE, retrieved from https://ieeexplore.ieee.org/document/8784036.
Long, Feng, “Research and Application of Cloud Storage Technology in University Information Service,” Chinese Excellent Masters' Theses Full-text Database, Mar. 2013, 72 pages, China Academic Journals Electronic Publishing House, China.
Non-Published Commonly Owned U.S. Appl. No. 17/562,890, filed Dec. 27, 2021, 36 pages, Nicira, Inc.
Non-Published Commonly Owned U.S. Appl. No. 17/572,583, filed Jan. 10, 2022, 33 pages, Nicira, Inc.
Noormohammadpour, Mohammad, et al., “DCRoute: Speeding up Inter-Datacenter Traffic Allocation while Guaranteeing Deadlines,” 2016 IEEE 23rd International Conference on High Performance Computing (HiPC), Dec. 19-22, 2016, 9 pages, IEEE, Hyderabad, India.
Guo, Xiangyi, et al., (U.S. Appl. No. 62/925,193), filed Oct. 23, 2019, 26 pages.
Lasserre, Marc, et al., “Framework for Data Center (DC) Network Virtualization,” RFC 7365, Oct. 2014, 26 pages, IETF.
Lin, Weidong, et al., “Using Path Label Routing in Wide Area Software-Defined Networks with Open Flow,” 2016 International Conference on Networking and Network Applications, Jul. 2016, 6 pages, IEEE.
Non-Published Commonly Owned U.S. Appl. No. 17/943,147, filed Sep. 12, 2022, 42 pages, Nicira, Inc.
Non-Published Commonly Owned U.S. Appl. No. 18/083,536, filed Dec. 18, 2022, 27 pages, VMware, Inc.
Taleb, Tarik, “D4.1 Mobile Network Cloud Component Design,” Mobile Cloud Networking, Nov. 8, 2013, 210 pages, MobileCloud Networking Consortium, retrieved from http://www.mobile-cloud-networking.eu/site/index.php?process=download&id=127&code=89d30565cd2ce087d3f8e95f9ad683066510a61f.
Valtulina, Luca, “Seamless Distributed Mobility Management (DMM) Solution in Cloud Based LTE Systems,” Master Thesis, Nov. 2013, 168 pages, University of Twente, retrieved from http://essay.utwente.nl/64411/1/Luca_Valtulina_MSc_Report_final.pdf.
Zakurdaev, Gieorgi, et al., “Dynamic On-Demand Virtual Extensible LAN Tunnels via Software-Defined Wide Area Networks,” 2022 IEEE 12th Annual Computing and Communication Workshop and Conference, Jan. 26-29, 2022, 6 pages, IEEE, Las Vegas, NV, USA.
Non-Published Commonly Owned U.S. Appl. No. 18/102,685, filed Jan. 28, 2023, 124 pages, VMware, Inc.
Del Piccolo, Valentin, et al., “A Survey of Network Isolation Solutions for Multi-Tenant Data Centers,” IEEE Communications Society, Apr. 20, 2016, vol. 18, No. 4, 37 pages, IEEE.
Fortz, Bernard, et al., “Internet Traffic Engineering by Optimizing OSPF Weights,” Proceedings IEEE INFOCOM 2000, Conference on Computer Communications, Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies, Mar. 26-30, 2000, 11 pages, IEEE, Tel Aviv, Israel, Israel.
Francois, Frederic, et al., “Optimizing Secure SDN-enabled Inter-Data Centre Overlay Networks through Cognitive Routing,” 2016 IEEE 24th International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS), Sep. 19-21, 2016, 10 pages, IEEE, London, UK.
Huang, Cancan, et al., “Modification of Q.SD-WAN,” Rapporteur Group Meeting—Doc, Study Period 2017-2020, Q4/11-DOC1 (190410), Study Group 11, Apr. 10, 2019, 19 pages, International Telecommunication Union, Geneva, Switzerland.
Michael, Nithin, et al., “HALO: Hop-by-Hop Adaptive Link-State Optimal Routing,” IEEE/ACM Transactions on Networking, Dec. 2015, 14 pages, vol. 23, No. 6, IEEE.
Mishra, Mayank, et al., “Managing Network Reservation for Tenants in Oversubscribed Clouds,” 2013 IEEE 21st International Symposium on Modelling, Analysis and Simulation of Computer and Telecommunication Systems, Aug. 14-16, 2013, 10 pages, IEEE, San Francisco, CA, USA.
Mudigonda, Jayaram, et al., “NetLord: A Scalable Multi-Tenant Network Architecture for Virtualized Datacenters,” Proceedings of the ACM SIGCOMM 2011 Conference, Aug. 15-19, 2011, 12 pages, ACM, Toronto, Canada.
Non-Published Commonly Owned U.S. Appl. No. 17/072,764, filed Oct. 16, 2020, 33 pages, VMware, Inc.
Non-Published Commonly Owned U.S. Appl. No. 17/072,774, filed Oct. 16, 2020, 34 pages, VMware, Inc.
Non-Published Commonly Owned U.S. Appl. No. 17/085,893, filed Oct. 30, 2020, 34 pages, VMware, Inc.
Non-Published Commonly Owned U.S. Appl. No. 17/085,916, filed Oct. 30, 2020, 35 pages, VMware, Inc.
Non-Published Commonly Owned U.S. Appl. No. 17/103,614, filed Nov. 24, 2020, 38 pages, VMware, Inc.
Non-Published Commonly Owned U.S. Appl. No. 17/143,092, filed Jan. 6, 2021, 42 pages, VMware, Inc.
Non-Published Commonly Owned U.S. Appl. No. 17/143,094, filed Jan. 6, 2021, 42 pages, VMware, Inc.
Non-Published Commonly Owned U.S. Appl. No. 17/194,038 , filed Mar. 5, 2021, 35 pages, VMware, Inc.
Non-Published Commonly Owned U.S. Appl. No. 17/227,016, filed Apr. 9, 2021, 37 pages, VMware, Inc.
Non-Published Commonly Owned U.S. Appl. No. 17/227,044, filed Apr. 9, 2021, 37 pages, VMware, Inc.
Non-Published Commonly Owned U.S. Appl. No. 17/240,890, filed Apr. 26, 2021, 325 pages, VMware, Inc.
Non-Published Commonly Owned U.S. Appl. No. 17/240,906, filed Apr. 26, 2021, 18 pages, VMware, Inc.
Non-Published Commonly Owned U.S. Appl. No. 17/351,327, filed Jun. 18, 2021, 48 pages, VMware, Inc.
Non-Published Commonly Owned U.S. Appl. No. 17/351,333, filed Jun. 18, 2021, 47 pages, VMware, Inc.
Non-Published Commonly Owned U.S. Appl. No. 17/351,340, filed Jun. 18, 2021, 48 pages, VMware, Inc.
Non-Published Commonly Owned U.S. Appl. No. 17/351,342, filed Jun. 18, 2021, 47 pages, VMware, Inc.
Non-Published Commonly Owned U.S. Appl. No. 17/351,345, filed Jun. 18, 2021, 48 pages, VMware, Inc.
Non-Published Commonly Owned U.S. Appl. No. 17/361,292, filed Jun. 28, 2021, 35 pages, Nicira, Inc.
Non-Published Commonly Owned U.S. Appl. No. 17/384,735, filed Jul. 24, 2021, 62 pages, VMware, Inc.
Non-Published Commonly Owned U.S. Appl. No. 17/384,736, filed Jul. 24, 2021, 63 pages, VMware, Inc.
Non-Published Commonly Owned U.S. Appl. No. 17/384,737, filed Jul. 24, 2021, 63 pages, VMware, Inc.
Non-Published Commonly Owned U.S. Appl. No. 17/384,738, filed Jul. 24, 2021, 62 pages, VMware, Inc.
Non-Published Commonly Owned U.S. Appl. No. 15/803,964, filed Nov. 6, 2017, 15 pages, The Mode Group.
PCT International Search Report and Written Opinion of Commonly Owned International Patent Application PCT/US2019/059563, dated Mar. 30, 2020, 12 pages, International Searching Authority (EPO).
PCT Invitation to Pay Additional Fees for Commonly Owned International Patent Application PCT/US2018/053811, dated Dec. 19, 2018, 13 pages, International Searching Authority (EPO).
Ray, Saikat, et al., “Always Acyclic Distributed Path Computation,” University of Pennsylvania Department of Electrical and Systems Engineering Technical Report, May 2008, 16 pages, University of Pennsylvania ScholarlyCommons.
Sarhan, Soliman Abd Elmonsef, et al., “Data Inspection in SDN Network,” 2018 13th International Conference on Computer Engineering and Systems (ICCES), Dec. 18-19, 2018, 6 pages, IEEE, Cairo, Egypt.
Webb, Kevin C., et al., “Blender: Upgrading Tenant-Based Data Center Networking,” 2014 ACM/IEEE Symposium on Architectures for Networking and Communications Systems (ANCS), Oct. 20-21, 2014, 11 pages, IEEE, Marina del Rey, CA, USA.
Kie, Junfeng, et al., A Survey of Machine Learning Techniques Applied to Software Defined Networking (SDN): Research Issues and Challenges, IEEE Communications Surveys & Tutorials, Aug. 23, 2018, 38 pages, vol. 21, Issue 1, IEEE.
Yap, Kok-Kiong, et al., “Taking the Edge off with Espresso: Scale, Reliability and Programmability for Global Internet Peering,” SIGCOMM '17: Proceedings of the Conference of the ACM Special Interest Group on Data Communication, Aug. 21-25, 2017, 14 pages, Los Angeles, CA.
Author Unknown, “VeloCloud Administration Guide: VMware SD-WAN by VeloCloud 3.3,” Month Unknown 2019, 366 pages, VMware, Inc., Palo Alto, CA, USA.
Funabiki, Nobuo, et al., “A Frame Aggregation Extension of Routing Algorithm for Wireless Mesh Networks,” 2014 Second International Symposium on Computing and Networking, Dec. 10-12, 2014, 5 pages, IEEE, Shizuoka, Japan.
Non-Published Commonly Owned U.S. Appl. No. 18/197,090, filed May 14, 2023, 36 pages, Nicira. Inc.

Related Publications (1)

	Number	Date	Country
	20210400113 A1	Dec 2021	US

Provisional Applications (1)

	Number	Date	Country
	62566524	Oct 2017	US

Continuations (2)

	Number	Date	Country
Parent	16405986	May 2019	US
Child	17467378		US
Parent	16252696	Jan 2019	US
Child	16405986		US

Continuation in Parts (2)

	Number	Date	Country
Parent	16192774	Nov 2018	US
Child	16252696		US
Parent	15972083	May 2018	US
Child	16192774		US

Layer four optimization for a virtual network defined over public cloud

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