Patent Grant
11516049
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.
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 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. 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.
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.
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.
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.
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 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.
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.
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.).
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.
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.
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.
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. This virtual network 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
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.
Through the configured components of the MFNs, the virtual network 100 of
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.
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
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
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
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.
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).
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.
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.
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 Ci points to a next-hop MFN CFE Cj, the CFE Ci considers Cj as a neighbor. In some embodiments, the CFE Ci establishes a secure, actively maintained VPN tunnel to CFE Cj. 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.
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).
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
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.
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
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
The processes 900-1100 will be explained below by reference to two examples in
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
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.
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.
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).
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
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.
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
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
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.
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
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.
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.
In the example illustrated in
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 232 region 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:
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
In the example illustrated in
The TM engines 1605 and 1705 operate similarly in
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).
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
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
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
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.
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 (TBWn) associated with this node and the amount of bandwidth already reserved (allocated to a certain reserved path) by this node (RBWn). In addition, for each node, the graph includes the cost in cents per gigabyte (Cij) and the delay in milliseconds (Dij) 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 Wij=a*Cij+Dij, 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 W1.
The second lowest-cost (e.g., shortest path) computation initially modifies the graph by eliminating all nodes i where BW>TBWi−RBWi. 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 TBWi−RBWi, 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 RBWi where 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.
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 2145; otherwise, the process returns to 2135 to determine whether it has received new measurements from the MFN measurement agents. 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.
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.
The bus 2205 collectively represents all system, peripheral, and chipset buses that communicatively connect the numerous internal devices of the computer system 2200. For instance, the bus 2205 communicatively connects the processing unit(s) 2210 with the read-only memory 2230, the system memory 2225, and the permanent storage device 2235.
From these various memory units, the processing unit(s) 2210 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) 2230 stores static data and instructions that are needed by the processing unit(s) 2210 and other modules of the computer system. The permanent storage device 2235, 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 2200 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 2235.
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 2235, the system memory 2225 is a read-and-write memory device. However, unlike storage device 2235, 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 2225, the permanent storage device 2235, and/or the read-only memory 2230. From these various memory units, the processing unit(s) 2210 retrieve instructions to execute and data to process in order to execute the processes of some embodiments.
The bus 2205 also connects to the input and output devices 2240 and 2245. The input devices enable the user to communicate information and select commands to the computer system. The input devices 2240 include alphanumeric keyboards and pointing devices (also called “cursor control devices”). The output devices 2245 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
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
| 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 |
| 7962458 | Holenstein et al. | Jun 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 |
| 8566452 | Goodwin et al. | Oct 2013 | B1 |
| 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 |
| 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 |
| 9055000 | Ghosh et al. | Jun 2015 | B1 |
| 9071607 | Twitchell, Jr. | Jun 2015 | B2 |
| 9075771 | Gawali et al. | Jul 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 |
| 9306949 | Richard | 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 10135789 | Mayya et al. | Nov 2018 | B2 |
| 10142226 | Wu et al. | Nov 2018 | B1 |
| 10178032 | Freitas | Jan 2019 | B1 |
| 10187289 | Chen et al. | Jan 2019 | B1 |
| 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 |
| 10461993 | Turabi et al. | Oct 2019 | B2 |
| 10511546 | Singarayan 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 |
| 10594659 | El-Moussa et al. | Mar 2020 | B2 |
| 10637889 | Ermagan et al. | Apr 2020 | B2 |
| 10693739 | Naseri et al. | Jun 2020 | B1 |
| 10778557 | Ganichev et al. | Sep 2020 | B2 |
| 10819564 | Turabi et al. | Oct 2020 | B2 |
| 10826775 | Moreno et al. | Nov 2020 | B1 |
| 10911374 | Kumar et al. | Feb 2021 | B1 |
| 10951529 | Duan et al. | Mar 2021 | B2 |
| 11050644 | Hegde et al. | Jun 2021 | B2 |
| 11071005 | Shen et al. | Jul 2021 | B2 |
| 11095612 | Oswal et al. | Aug 2021 | B1 |
| 11108851 | Kurmala et al. | Aug 2021 | B1 |
| 11115347 | Gupta et al. | Sep 2021 | B2 |
| 11115426 | Pazhyannur et al. | Sep 2021 | B1 |
| 11128492 | Sethi et al. | Sep 2021 | B2 |
| 11381474 | Kumar et al. | Jul 2022 | B1 |
| 20020085488 | Kobayashi | Jul 2002 | A1 |
| 20020087716 | Mustafa | Jul 2002 | A1 |
| 20020198840 | Banka et al. | Dec 2002 | 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 |
| 20050265255 | Kodialam et al. | Dec 2005 | A1 |
| 20060002291 | Alicherry et al. | Jan 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 |
| 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 |
| 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 |
| 20080181116 | Kavanaugh et al. | Jul 2008 | A1 |
| 20080219276 | Shah | Sep 2008 | A1 |
| 20080240121 | Xiong et al. | Oct 2008 | A1 |
| 20090013210 | McIntosh et al. | Jan 2009 | A1 |
| 20090125617 | Klessig et al. | May 2009 | A1 |
| 20090141642 | Sun | Jun 2009 | A1 |
| 20090154463 | Hines et al. | Jun 2009 | A1 |
| 20090247204 | Sennett 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 |
| 20100080129 | Strahan et al. | Apr 2010 | A1 |
| 20100088440 | Banks et al. | 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 |
| 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 |
| 20110007752 | Silva et al. | Jan 2011 | A1 |
| 20110032939 | Nozaki et al. | Feb 2011 | A1 |
| 20110040814 | Higgins | Feb 2011 | A1 |
| 20110075674 | Li 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 |
| 20120008630 | Ould-Brahim | Jan 2012 | A1 |
| 20120027013 | Napierala | Feb 2012 | A1 |
| 20120136697 | Peles et al. | May 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 |
| 20120250682 | Vincent et al. | Oct 2012 | A1 |
| 20120250686 | Vincent 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 | Kamada | Dec 2012 | A1 |
| 20120317270 | Vrbaski et al. | Dec 2012 | A1 |
| 20120317291 | Wolfe | Dec 2012 | 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 |
| 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 |
| 20130173788 | Song | Jul 2013 | A1 |
| 20130182712 | Aguayo et al. | Jul 2013 | A1 |
| 20130191688 | Agarwal et al. | Jul 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 | 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 |
| 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 |
| 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 |
| 20140164560 | Ko et al. | Jun 2014 | A1 |
| 20140164617 | Jalan 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 |
| 20140208317 | Nakagawa | Jul 2014 | A1 |
| 20140219135 | Li et al. | Aug 2014 | A1 |
| 20140223507 | Xu | 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 |
| 20140317440 | Biermayr et al. | Oct 2014 | A1 |
| 20140321277 | Lynn, Jr. et al. | Oct 2014 | A1 |
| 20140337500 | Lee | Nov 2014 | A1 |
| 20140341109 | Cartmell et al. | Nov 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 20160080502 | Yadav et al. | Mar 2016 | A1 |
| 20160105353 | Cociglio | Apr 2016 | A1 |
| 20160105471 | Nunes et al. | Apr 2016 | A1 |
| 20160117185 | Fang et al. | Apr 2016 | A1 |
| 20160134461 | Sampath et al. | May 2016 | A1 |
| 20160134528 | Lin et al. | May 2016 | A1 |
| 20160134591 | Liao et al. | May 2016 | A1 |
| 20160142373 | Ossipov | May 2016 | A1 |
| 20160150055 | Choi | May 2016 | A1 |
| 20160164832 | Bellagamba et al. | Jun 2016 | A1 |
| 20160164914 | Madhav | 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 |
| 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 |
| 20160255169 | Kovvuri 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 |
| 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 |
| 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 |
| 20170026283 | Williams 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 |
| 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 |
| 20170118173 | Arramreddy et al. | Apr 2017 | A1 |
| 20170123939 | Maheshwari 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 |
| 20170163473 | Sadana et al. | Jun 2017 | A1 |
| 20170171310 | Gardner | Jun 2017 | A1 |
| 20170181210 | Nadella et al. | Jun 2017 | A1 |
| 20170195161 | Ruel et al. | Jul 2017 | A1 |
| 20170195169 | Mills 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 |
| 20170237710 | Mayya 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 |
| 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 |
| 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 |
| 20170337086 | Zhu et al. | Nov 2017 | A1 |
| 20170339054 | Yadav et al. | Nov 2017 | A1 |
| 20170339070 | Chang et al. | Nov 2017 | A1 |
| 20170364419 | Lo | Dec 2017 | A1 |
| 20170366445 | Nemirovsky et al. | Dec 2017 | A1 |
| 20170366467 | Martin 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 |
| 20180097725 | Wood et al. | Apr 2018 | A1 |
| 20180114569 | Strachan et al. | Apr 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 |
| 20180176082 | Katz et al. | Jun 2018 | A1 |
| 20180176130 | Banerjee et al. | Jun 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 |
| 20180260125 | Botes 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 |
| 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 |
| 20190020627 | Yuan | 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 |
| 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 |
| 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 |
| 20190140889 | Mayya et al. | May 2019 | A1 |
| 20190140890 | Mayya 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 |
| 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 |
| 20190268421 | Markuze et al. | Aug 2019 | A1 |
| 20190268973 | Bull et al. | Aug 2019 | A1 |
| 20190280962 | Michael et al. | Sep 2019 | A1 |
| 20190280963 | Michael et al. | Sep 2019 | A1 |
| 20190280964 | Michael et al. | Sep 2019 | A1 |
| 20190306197 | Degioanni | Oct 2019 | A1 |
| 20190313907 | Khachaturian et al. | Oct 2019 | A1 |
| 20190319847 | Nahar et al. | Oct 2019 | A1 |
| 20190327109 | Guichard et al. | Oct 2019 | A1 |
| 20190334813 | Raj et al. | Oct 2019 | A1 |
| 20190334820 | Zhao | Oct 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 |
| 20200014609 | Hockett et al. | Jan 2020 | A1 |
| 20200014615 | Michael et al. | Jan 2020 | A1 |
| 20200014616 | Michael 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 |
| 20200059420 | Abraham | Feb 2020 | A1 |
| 20200059457 | Raza et al. | Feb 2020 | A1 |
| 20200059459 | Abraham et al. | Feb 2020 | A1 |
| 20200092207 | Sipra et al. | Mar 2020 | A1 |
| 20200097327 | Beyer et al. | Mar 2020 | A1 |
| 20200099659 | Cometto et al. | Mar 2020 | A1 |
| 20200106696 | Michael 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 |
| 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 |
| 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 |
| 20200236046 | Jain 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 |
| 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 |
| 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 |
| 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 |
| 20200382345 | Zhao et al. | Dec 2020 | A1 |
| 20200382387 | Pasupathy 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 |
| 20210029019 | Kottapalli | Jan 2021 | A1 |
| 20210036888 | Makkalla et al. | Feb 2021 | A1 |
| 20210036987 | Mishra et al. | Feb 2021 | A1 |
| 20210067442 | Sundararajan et al. | Mar 2021 | A1 |
| 20210105199 | H et al. | Apr 2021 | A1 |
| 20210112034 | Sundararajan et al. | Apr 2021 | A1 |
| 20210126830 | R. et al. | Apr 2021 | A1 |
| 20210126854 | Guo 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 |
| 20210194814 | Roux et al. | Jun 2021 | A1 |
| 20210226880 | Ramamoorthy 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 |
| 20210336880 | Gupta et al. | Oct 2021 | A1 |
| 20210377109 | Shrivastava et al. | Dec 2021 | A1 |
| 20210392070 | Tootaghaj et al. | Dec 2021 | A1 |
| 20210399920 | Sundararajan et al. | Dec 2021 | A1 |
| 20220038370 | Vasseur et al. | Feb 2022 | A1 |
| Number | Date | Country |
|---|---|---|
| 1926809 | Mar 2007 | CN |
| 102811165 | Dec 2012 | CN |
| 104956329 | Sep 2015 | CN |
| 106656847 | May 2017 | CN |
| 110447209 | Nov 2019 | CN |
| 111198764 | May 2020 | CN |
| 1912381 | Apr 2008 | EP |
| 3041178 | Jul 2016 | EP |
| 3509256 | Jul 2019 | EP |
| 2010233126 | Oct 2010 | JP |
| 2017059991 | Mar 2017 | JP |
| 2574350 | Feb 2016 | RU |
| 03073701 | Sep 2003 | WO |
| 2012167184 | Dec 2012 | WO |
| 2016061546 | Apr 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 |
| 2020112345 | Jun 2020 | WO |
| 2021211906 | Oct 2021 | WO |
| Entry |
|---|
| Non-published commonly owned U.S. Appl. No. 16/252,696, filed Jan. 20, 2019, 145 pages, Nicira, Inc. |
| 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 International Patent Application PCT/US2018/053811, filed Oct. 1, 2018, 92 pages, Nicira, Inc. |
| Non-Published commonly owned U.S. Appl. No. 16/192,774, filed Nov. 15, 2018, 116 pages, Nicira, Inc. |
| Non-Published commonly owned U.S. Appl. No. 16/192,780, filed Nov. 15, 2018, 116 pages, Nicira, Inc. |
| Non-Published commonly owned U.S. Appl. No. 16/192,783, filed Nov. 15, 2018, 116 pages, Nicira, Inc. |
| 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). |
| Non-Published commonly Owned U.S. Appl. No. 15/701,115, filed Sep. 11, 2017,21 pages, Nicira, Inc. |
| Non-Published commonly Owned U.S. Appl. No. 15/707,124, filed Sep. 18, 2017,24 pages, Nicira, Inc. |
| Non-Published commonly Owned U.S. Appl. No. 15/784,404, filed Oct. 16, 2017,21 pages, Nicira, Inc. |
| Non-Published commonly Owned U.S. Appl. No. 15/811,329, filed Nov. 13, 2017, 37 pages, Nicira, Inc. |
| Non-Published commonly Owned U.S. Appl. No. 15/838,052, filed Dec. 11, 2017, 28 pages, Nicira, Inc. |
| Non-Published commonly Owned U.S. Appl. No. 15/838,355, filed Dec. 11, 2017, 29 pages, Nicira, Inc. |
| Non-Published commonly Owned U.S. Appl. No. 15/972,083, filed May 4, 2018, 93 pages, Nicira, Inc. |
| Non-Published commonly Owned U.S. Appl. No. 15/972,088, filed May 4, 2018, 94 pages, Nicira, Inc. |
| Non-Published commonly Owned U.S. Appl. No. 15/972,090, filed May 4, 2018, 93 pages, Nicira, Inc. |
| Non-Published commonly Owned U.S. Appl. No. 15/972,091, filed May 4, 2018, 94 pages, Nicira, Inc. |
| Non-Published commonly Owned U.S. Appl. No. 15/972,093, filed May 4, 2018, 93 pages, Nicira, Inc. |
| Non-Published commonly Owned U.S. Appl. No. 15/972,095, filed May 4, 2018, 93 pages, Nicira, Inc. |
| Non-Published commonly Owned U.S. Appl. No. 15/972,098, filed May 4, 2018, 94 pages, Nicira, Inc. |
| Non-Published commonly Owned U.S. Appl. No. 15/972,100, filed May 4, 2018, 93 pages, Nicira, Inc. |
| Non-Published commonly Owned U.S. Appl. No. 15/972,102, filed May 4, 2018, 93 pages, Nicira, Inc. |
| Non-Published commonly Owned U.S. Appl. No. 15/972,103, filed May 4, 2018, 93 pages, Nicira, Inc. |
| Non-Published commonly Owned U.S. Appl. No. 15/972,104, filed May 4, 2018, 93 pages, Nicira, Inc. |
| 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). |
| 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. |
| 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. |
| 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. |
| 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. |
| 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. |
| 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. |
| 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. |
| 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. |
| 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. |
| Guo, Xiangyi, et al., (U.S. Provisional 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. |
| 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. |
| Xie, 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. |
| 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. |
| 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. |
| Number | Date | Country | |
|---|---|---|---|
| 20190104049 A1 | Apr 2019 | US |
| Number | Date | Country | |
|---|---|---|---|
| 62566524 | Oct 2017 | US |
