Explicit congestion notification in a virtual environment

Information

  • Patent Grant
  • 12184557
  • Patent Number
    12,184,557
  • Date Filed
    Saturday, December 24, 2022
    2 years ago
  • Date Issued
    Tuesday, December 31, 2024
    3 days ago
Abstract
Some embodiments provide a method of reducing network congestion in a virtual network. The method, at a first CFE of the virtual network, receives multiple encapsulated data packets of a data stream. The encapsulated data packets having been encapsulated by a second CFE, operating on a server of the virtual network. The second CFE identifies a load percentage of the server, sets explicit congestion notification (ECN) bits on a percentage of the data packets based on the load percentage of the server, and encapsulates each data packet. The first CFE determines whether to forward a new connection to the second CFE based at least on the percentage of data packets from the first CFE with the ECN bits set.
Description

In networks that transmit data using internet protocol (IP) packet transmission, multiple routers between the source and destination points of data packets receive and forward the data packets. Each of these routers has a finite capacity for handling data packets and it is possible for a router to receive more data packets in a given period of time than that router is capable of handling. This is referred to as network congestion. In the IP standard, network congestion is handled in various ways. In some IP systems, congested routers drop excess packets, resulting in the destination of the packets failing to acknowledge receipt of those packets. In response to the dropped packets, the source of the data packets reduces the frequency at which it sends subsequent packets.


A more efficient alternative to responding to dropped packets is to use explicit congestion notification (ECN). In an ECN enabled system, all routers along a path between a source and a destination make use of a dedicated set of 2 ECN bits in the IP standard header. These ECN bits are set to binary values 10 or 01 in each packet to indicate that the endpoints support ECN. When a router along the path of a data stream is congested when a packet arrives, the router changes the ECN value to a binary value of 11 to indicate congestion along the route. This status is reported to the source as an ECN echo (ECE) bit in acknowledgement (ACK) packets sent back by the destination. The ACK packets with ECE bits indicating congestion are treated by the source in a similar manner to dropped packets. That is, the source reduces the frequency at which it sends subsequent packets. One of ordinary skill in the art will understand that there is an ECN system implemented as part of an existing extension of internet protocols (IP). Some embodiments work with the existing ECN system. However other embodiments will work with an updated, modified, or otherwise different ECN system than the existing ECN system.


When a packet is sent through a virtual tunnel (sometimes called an “overlay tunnel” or a “VPN tunnel”) of a VPN, the original IP packet headers are encapsulated (e.g., encrypted) at a cloud forwarding element (CFE) of the VPN along with the payload of the data packets. A new header is then prepended to the encapsulated packet. The new header is used by the physical network underlying the VPN and includes ECN bits which the routers of the underlying physical network may use to identify congestion. However, the routers of the physical network are unable to recognize the encapsulated ECN bits of the original packet. Accordingly the encapsulated ECN bits may be repurposed in a method of reducing network congestion in a virtual network by efficiently assigning new data packet streams to receiving CFEs of the VPN tunnels with more available resources.


BRIEF SUMMARY

Some embodiments provide a method of reducing network congestion in a virtual network. The method, at a first CFE of the virtual network, receives multiple encapsulated data packets of a data stream, the encapsulated data packets having been encapsulated by a second CFE, operating on a server of the virtual network. The second CFE identifies a load percentage of the server, sets explicit congestion notification (ECN) bits on a percentage of non-encapsulated data packets based on the load percentage of the server, and encapsulates each non-encapsulated data packet. The first CFE determines whether to forward a new connection to the second CFE based at least on the percentage of data packets from the first CFE with the ECN bits set. In some embodiments, the load percentage is the larger of (i) a percentage of CPU resources of the server in use and (ii) a percentage of memory resources of the server in use. In other embodiments, the second CFE uses the first ECN bit of each of multiple packets to indicate the memory usage percentage of the server and uses the second ECN bit of each of the packets to indicate the CPU usage percentage, rather than both bits indicating a single load percentage value. The encapsulated data packets, in some embodiments are encapsulated acknowledgement (ACK) packets each sent in response to a set of encapsulated packets (e.g., one ACK packet for every two original encapsulated packets) received by the second CFE from the first CFE.


The method of some embodiments sets the ECN bits on a percentage of the data packets by randomly determining, for each data packet, whether to set an ECN bit of the data packet to a positive status or a negative status with a probability to set the ECN bit to a positive status being based on the load percentage. In some embodiments, the probability to set the ECN bit to a positive status is equal to the load percentage.


The second CFE may be one of a set of CFEs able to process data packets of the new connection. Each CFE of the set of CFEs sets data packets to the first CFE with a percentage of the data packets having an ECN bit set. The method, in some embodiments, determines whether to forward the new connection to the second CFE by comparing the percentage of data packets from the second CFE with the ECN bits set to percentages of data packets from each of the CFEs in the set of CFEs with the ECN bits set. For example, the method may compare the percentage of data packets from the second CFE with the ECN bits set to percentages of data packets from each of the CFEs in the set of CFEs with the ECN bits set by calculating a relative free capacity of the second CFE. The relative free capacity may be determined by calculating a free capacity for each CFE and dividing the free capacity of the second CFE by the sum of the free capacities of the CFEs in the set of CFEs. Determining whether to forward the new connection to the second CFE may be performed by randomly assigning the new connection either to the second CFE or to another CFE of the set of CFEs, with the probability of assigning the new connection to the second CFE being based on the relative free capacity of the second CFE.


The ECN bits of an encapsulated data packet may be a first encapsulated set of ECN bits and the encapsulated data packet may further include a second, non-encapsulated set of ECN bits. A hardware routing system underlies the virtual network. The second, non-encapsulated set of ECN bits, in some embodiments, is identifiable by the hardware routing system, and the first, encapsulated set of ECN bits is not identifiable by the hardware routing system.


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, the 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, the Detailed Description, and the Drawings.





BRIEF DESCRIPTION OF THE DRAWINGS

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



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



FIG. 2 conceptually illustrates a process of some embodiments for determining a destination managed forwarding node (MFN) to send data packets to.



FIG. 3 illustrates communications using acknowledgement packets among a set of datacenters with MFNs.



FIG. 4 illustrates examples of an encapsulated packet and a non-encapsulated packet received by and sent by a CFE of a VPN.



FIG. 5 illustrates an example of a managed forwarding node of some embodiments of the invention.



FIG. 6 illustrates MFN selection elements of some embodiments.



FIG. 7 conceptually illustrates a computer system with which some embodiments of the invention are implemented.





DETAILED DESCRIPTION

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


Some embodiments provide a method of reducing network congestion in a virtual network. The method, at a first CFE of the virtual network, receives multiple encapsulated data packets of a data stream, the encapsulated data packets having been encapsulated by a second CFE, operating on a server of the virtual network. The second CFE identifies a load percentage of the server, sets explicit congestion notification (ECN) bits on a percentage of non-encapsulated data packets based on the load percentage of the server, and encapsulates each non-encapsulated data packet. The first CFE determines whether to forward a new connection to the second CFE based at least on the percentage of data packets from the first CFE with the ECN bits set. In some embodiments, the load percentage is the larger of (i) a percentage of CPU resources of the server in use and (ii) a percentage of memory resources of the server in use. In other embodiments, the second CFE uses the first ECN bit of each of multiple packets to indicate the memory usage percentage of the server and uses the second ECN bit of each of the packets to indicate the CPU usage percentage, rather than both bits indicating a single load percentage value. The encapsulated data packets, in some embodiments are encapsulated acknowledgement (ACK) packets each sent in response to a set of encapsulated packets (e.g., one ACK packet for every two original encapsulated packets) received by the second CFE from the first CFE.


The method of some embodiments sets the ECN bits on a percentage of the data packets by randomly determining, for each data packet, whether to set an ECN bit of the data packet to a positive status or a negative status with a probability to set the ECN bit to a positive status being based on the load percentage. In some embodiments, the probability to set the ECN bit to a positive status is equal to the load percentage.


The second CFE may be one of a set of CFEs able to process data packets of the new connection. Each CFE of the set of CFEs sets data packets to the first CFE with a percentage of the data packets having an ECN bit set. The method, in some embodiments, determines whether to forward the new connection to the second CFE by comparing the percentage of data packets from the second CFE with the ECN bits set to percentages of data packets from each of the CFEs in the set of CFEs with the ECN bits set. For example, the method may compare the percentage of data packets from the second CFE with the ECN bits set to percentages of data packets from each of the CFEs in the set of CFEs with the ECN bits set by calculating a relative free capacity of the second CFE. The relative free capacity may be determined by calculating a free capacity for each CFE and dividing the free capacity of the second CFE by the sum of the free capacities of the CFEs in the set of CFEs. Determining whether to forward the new connection to the second CFE may be performed by randomly assigning the new connection either to the second CFE or to another CFE of the set of CFEs, with the probability of assigning the new connection to the second CFE being based on the relative free capacity of the second CFE.


The ECN bits of an encapsulated data packet may be a first encapsulated set of ECN bits and the encapsulated data packet may further include a second, non-encapsulated set of ECN bits. A hardware routing system underlies the virtual network. The second, non-encapsulated set of ECN bits, in some embodiments, is identifiable by the hardware routing system, and the first, encapsulated set of ECN bits is not identifiable by the hardware routing system.


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 may be used herein to refer to various formatted collections of bits that may be sent across a network, such as 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, 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. Network traffic refers to a set of data packets sent through a network. For example, network traffic could be sent from an application operating on a machine (e.g., a virtual machine or physical computer) on a branch of an SD-WAN through a hub node of a hub cluster of the SD-WAN.



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


One of ordinary skill in the art will understand that the overlay tunnels are not physical entities, but instead are conceptual tunnels that are used to represent the actions of a CFE of the VPN encrypting (sometimes called “encapsulating”) data packets at one end of the virtual tunnel so that only another CFE, conceptually represented as the other end of the tunnel, can de-encapsulate/decrypt the packets to restore the original data packets. While the packets may be transferred along many different physical routes through the underlying network(s), the contents are protected, from third party inspection, by the encapsulation.


As further described below, the group of components that form an MFN include in some embodiments (1) one or more 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 cloud forwarding elements (CFEs) for encapsulating data messages and 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. The CFEs in some embodiments are implemented by hardware routers, by software forwarding elements (e.g., software routers) executing on computers, by some other hardware, software, or combination of hardware or software that directs traffic through a physical or virtual network that implements a network cloud. Similarly, in some embodiments the actions described as being performed by CFEs may be performed by two or more hardware and/or software elements working together, such as a hardware router in conjunction with a software element working either on or outside the router to perform the encapsulation of packets.


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 close to their destinations while reducing their traversal through the Internet. When the corporate compute endpoints are outside of public cloud datacenters over which the virtual network spans, these endpoints are referred to as external machine locations. This is the case for corporate branch offices, private datacenters and devices of remote users.


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


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


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 100 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 100, 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. The potential for additional tenants to operate on the same public clouds increases the security risk of unencrypted packets, providing a further incentive for a client to use VPN tunnels to protect data from third parties.



FIG. 2 conceptually illustrates a process 200 of some embodiments for determining a destination MFN (and thus the CFE of the MFN) to send data packets to. Some operations of FIG. 2 will be described in relation to FIG. 3. FIG. 3 illustrates communications using acknowledgement packets among a set of datacenters 110a-110d with MFNs 150 and 305-315. The MFN 150 is determining which of the two candidate MFNs 305 and 310 should be the next hop on the VPN for sending a new stat stream to one of the destination machines 300. In order to gather data used to make this determination, the MFN 150 receives acknowledgment packets (ACK packets) 320 and 325 in acknowledgment of earlier data packets (not shown) of earlier data streams. Although not used in determining the next hop from MFN 150 toward destination machines 300 in the illustrated case, the MFN 315 similarly sends ACK packets 335 and 340 to MFNs 305 and 310, respectively. In FIG. 2, the process 200 is performed by a first CFE, which is sending an initial stream of IP data packets in communication with a second CFE which is receiving the initial stream of data packets and sending acknowledgement (ACK) data packets back to the first CFE in reply.


The process 200 begins when the first CFE sends (at 205) data packets through a virtual tunnel to the second CFE. As mentioned above, sending data packets through a virtual tunnel encapsulates the original data packets and provides a new header for the encapsulated packets to send the packets to another CFE of the VPN. This encapsulation prevents the data in the original packet from being inspected by third parties. The encapsulation also prevents the original header of the original packet, including the ECN bits (e.g., the 15th and 16th bits in the header in the IPv4 standard) from being read by physical routers along the physical path the encapsulated packets travel to the second CFE. However, in some embodiments, the new header includes an additional set of ECN bits, which allows the underlying physical network (if ECN is enabled for all routers along the physical path between the CFEs) to use the ECN system to handle congestion on the physical network.


The process 200 then continues when the second CFE receives (at 210) the data packets and generates an ACK packet for each of the received data packets. The process 200 then identifies (at 215) a load percentage of the server on which the second CFE is operating. This load percentage may be referred to herein as the load percentage of the CFE, or the MFN (for brevity), or of the server on which the MFN is operating. The load percentage in some embodiments is the larger of the percentage of the memory resources in use and the percentage of the CPU resources in use. For example, if 40% of the memory resources were in use and 50% of the CPU resources were in use, then the load percentage would be 50%. In some embodiments, the load percentage is the percentage of the resources available to the CFE that are being used. The resources available may be the total memory and CPU resources of the server, or they may be an amount of those resources specifically allocated to the CFE.


The process 200 then sets (at 220) an ECN bit to positive in each of a percentage of the ACK packets based on the load percentage. As used herein, setting the ECN bit to “positive” indicates that the bit has been set to a value that indicates (to the first CFE) that more resources are in use than if the bit were set to “negative.” The positive setting may be a binary value of 1 in some embodiments, however in alternate embodiments, the binary value of 0 may be the positive value, with a binary value of 1 for the ECN bits indicating more resources are free.


In some embodiments, the ECN bit set (at 220) is the first ECN bit in the header, in other embodiments the ECN bit is the second ECN bit in the header. Still other embodiments may set both ECN bits in each packet (e.g., for more precise signaling of the load percentage). The percentage of the ECN bits set to positive is identical to or approximately the same as the load percentage in some embodiments. However, in other embodiments, the percentage of ECN bits set to positive may be a different value derived from the load percentage. In some embodiments, the process determines, for each packet, whether to set the ECN bit(s) to a positive value based on random selection, with the probability of a positive setting being based on the load percentage.


The process 220 then encapsulates (at 225) the ACK packets and sends them through a virtual tunnel to the first CFE. In FIG. 3, this is shown by ACK packets 320 being sent from MFN 305 to MFN 150 and ACK packets 325 being sent from MFN 310 to MFN 150. Here, the load percentage of the server that implements MFN 305 is 90% and therefore, in this embodiment, 90% of the ACK packets 320 are sent with the ECN bits set, as indicated by the value p=0.9 of the ACK packet. Similarly, the load percentage of the server that implements MFN 310 is 60% and therefore, in this embodiment, 60% of the ACK packets 325 are sent with the ECN bits set, as indicated by the value p=0.6 of the ACK packet. One of ordinary skill in the art will understand that the ACK packets 320 and 325 are sometimes sent in response to entirely different data streams and that CFEs of each MFN 305 and 310 are acting as the “second CFE” in the process 200 of FIG. 2.


One of ordinary skill in the art will understand that in some embodiments, the load percentage of an MFN and thus the percentage of ACK packets from that MFN with the ECN bits set are a property of the MFN. Therefore at any given time, any ACK packets from a particular server to any other server will have the same (or approximately the same, owing to weighted random selections) percentage of ECN bits set. This is illustrated in FIG. 3, as the load percentage of MFN 315 is 70% and the percentage of ACK packets 335 and 340 with ECN bits set is 70%.


At the first CFE, the process 200 receives (at 230) and de-encapsulates the packets. The process 200 then determines (at 235) based on the percentage of ACK packets with ECN bits set to positive whether to forward a new connection to the second CFE. In other embodiments, the first CFE may base the determination on ACK packets with ECN bits set to negative. That is, the determination in some embodiments is based directly on the relative amount of free resources rather than the relative amount of used resources.


In some embodiments, the first CFE similarly receives ACK packets from multiple CFEs (e.g., the CFEs of MFN 305 or 310 in FIG. 3) that include the second CFE. When the first CFE receives a new data stream (sometimes called a “new connection”), a set of packets with the same 5-tuple or 4-tuple, including e.g., source and destination IPs, source and destination ports, and possibly protocol of the packets, the first CFE may assign any of a set of candidate CFEs as the “next hop” within the VPN for packets in that data stream. Each of the CFEs in the set is a potential “next hop” within the VPN for packets from the first CFE to the destination of the packets. In some embodiments, in order to determine which CFE of the set to use as the next hop for a, the first CFE determines the free resources of the candidate CFEs. In some embodiments, the first CFE identifies the free resources of a particular candidate CFE based on the percentage of encapsulated ENC bits set in the ACK packets received from that candidate CFE. For example, if the load percentage of a particular CFE A is based on the percentage of resources in use, the first CFE may use the following example equation or a similar equation to calculate a relative free capacity (RFC) of the CFE A compared to the other available options.

Relative Free Capacity A=(100%−load percent A %)/Sum(100%−load[A . . . Z]%)  (1)


That is, in such embodiments, the first CFE calculates a free resource capacity (e.g., the percent remaining given the load percentage) for each candidate CFE and for each CFE compares the free resource capacity of that CFE to the sum of the free resource capacities of all candidate CFEs (CFEs A to Z in equation (1)). In FIG. 3, the RFC for MFNs 305 and 310 are shown beside the ACK packets 320 and 325, respectively. These RFCs are shown to emphasize that they relate to the load percentages (p=0.9 and p=0.6) identified by the ACK packets 320 and 325. However, one of ordinary skill in the art will understand that unlike the load percentage, which is a property of the server of each MFN (at any given time), the RFC is based on the load percentages (and the consequent percent of ACK packets with ECN bits set) of the servers of multiple different MFNs. Therefore, the RFC value for a particular MFN can change even if the load percentage of that MFN changes.



FIG. 3 illustrates a stage where MFN 150 is determining which MFN node 305 or 310 to send a new data stream to. No RFC values are shown for ACK packets 335 and 340 because the next hop is determined at each MFN, in some embodiments. Therefore, until data packets of the new connection reach either MFN 305 or MFN 310, those MFNs do not have a reason to calculate RFC values for a new connection. Additionally, in some embodiments, data streams with different destinations may have different sets of next hops. Since the RFC calculations are based on multiple candidate MFNs, the RFCs are calculated once the candidate MFNs for the next hop of a given data stream are known. Additionally, in some embodiments, when there is only a single candidate MFN for a next hop for a data stream, that data stream is simply assigned to that MFN without calculating an RFC for that candidate.


Once the first CFE determines the relative free capacity of each candidate CFE, the first CFE in some embodiments uses those values (and other factors, in some embodiments) to determine which candidate CFE to assign a new connection to. In some embodiments, the first CFE assigns the new connection to one of the candidate CFEs at random, with the probability of selecting a particular CFE being equal to, or influenced by, the relative free capacity of that CFE. That is, in some embodiments, CFEs with a large percentage of free resources are more likely to be selected than CFEs with a small percentage of free resources. In other embodiments the CFE to assign a new connection to is selected using the load percentage in another way, for example by assigning the new connection to the CFE with the lowest load percentage.


The IPv4 and IPv6 standards each use two bits for ECN. In some embodiments, each bit is used for identifying the usage of a separate resource. For example, in some embodiments, the first encapsulated ECN bit is used to indicate the memory usage percentage and the second encapsulated ECN bit is used to indicate the CPU usage percentage. In such embodiments, after the ACK packets are de-encapsulated by the first CFE, the percentage of de-encapsulated packets with the first ECN bit set to positive (e.g., a binary value of 1) indicates to the first CFE what the memory usage percentage of the second CFE is, and the percentage of de-encapsulated packets with the second ECN bit set to positive indicates to the first CFE what the CPU usage percentage of the second CFE is. The first CFE then uses those values as factors to determine whether to assign a new connection to the second CFE. CFEs that implement the process 200 are further described in context of managed forwarding nodes with respect to FIG. 5, below.



FIG. 4 illustrates examples of an encapsulated packet 400 and a non-encapsulated packet 405 received by and sent by a CFE of a VPN. The non-encapsulated packet 400 is received by a CFE, for example from an application on a VM served by the CFE. The non-encapsulated packet 400 includes IP headers 410, TCP headers 420, and a payload 430. In some embodiments, this payload may include a number of bytes limited based a maximum transmission unit of the route over which the packet will be sent or the payload may be omitted for some packets. The IP headers are comprised of bits arranged according to an IP standard (e.g., IPv4 or IPv6). The IP headers include two ECN bits 415. The headers 410 and 420 and payload 430 in the non-encapsulated packet 400 would be readable if the non-encapsulated packet were sent out before applying encapsulation. However, as described with respect to FIG. 2, in the methods of the present embodiment, a CFE encapsulates the non-encapsulated packet 400 to generate encapsulated packet 405.


Once encapsulated, the IP headers 410, TCP headers 420, and payload 430 are encrypted as the payload of the encapsulated packet 405. The encapsulated packet 405 is then prepended with a new IP header 440 and TCP header 450. The IP header 440 includes a set of ECN bits 445 that are accessible by the underlying network routers between the CFEs of the VPN. If the encapsulated packet 405 is sent over underlying network routers that support ECN, then the ECN bits 445 may be used conventionally to identify congestion along the physical route between the CFEs. However, ECN bits 415 are part of the encrypted non-encapsulated packet 400. Therefore, underlying network routers could not access the ECN bits 415 and those ECN bits are available for use in the manner described with respect to FIG. 2 in implementing the methods of the present invention.


In some embodiments, an intermediate CFE between a sending CFE and a receiving CFE may be the end point of one VPN tunnel and the start of another VPN tunnel. In some such embodiments, the intermediate CFE de-encapsulates packets coming in on one tunnel (e.g., with one encryption key) from a sending CFE. The intermediate CFE then reads the decrypted ECN bits of the packets to determine the load percentage (or individual resource usage) of the sending CFE. The intermediate CFE then sets the ECN bits according to the load percentage of the intermediate CFE, before re-encapsulating the packet and sending the re-encapsulated packet to the receiving CFE. The receiving CFE then de-encapsulates the packet and determines the load percentage or resource usage of the intermediate CFE. Some embodiments may repeat this process through a series of CFEs, with each CFE retrieving load percentage or resource usage data from the ECN bits 415 before re-setting those bits to values that reflect the present CFE's load percentage or resource usage.



FIG. 5 illustrates an example of a managed forwarding node 150 of some embodiments of the invention. In some embodiments, each managed forwarding node 150 is a machine (e.g., a VM or container) that executes on a host computer in a public cloud datacenter. In other embodiments, each managed forwarding node 150 is implemented by multiple machines (e.g., multiple VMs or containers) that execute on the same host computer in one public cloud datacenter. In still other embodiments, two or more components of one MFN can be implemented by two or more machines executing on two or more host computers in one or more public cloud datacenters. As shown, the managed forwarding node 150 includes a firewall 510, one or more optimization engines 520, edge gateways 525 and 530, and a CFE 535 (e.g., a cloud router). In some embodiments, each of these components 505-535 can be implemented as a cluster of two or more components. Further description of managed forwarding nodes of some embodiments is found in U.S. Pat. No. 11,005,684, which is incorporated herein by reference.


In some embodiments, the branch gateway 525 and remote device gateway 530 establish secure IPsec connections (e.g., IPsec tunnels) respectively with one or more branch offices 130 and remote devices (e.g., mobile devices 140) that connect to the MFN 150. The branch gateways 525 and 530 receive data packets through the IPsec tunnel from the branch office 130 and mobile devices 140, respectively. The gateways 525 and 530 send the data packets through firewall 510 and optimization engine 520 to the CFE 535. The CFE 535 then performs the encapsulation process described above and sends the packets to the CFE of another MFN. In some embodiments, the CFE 535 sends the data packets to an MFN determined by the load balancing MFN selector 540 (e.g., an MFN determined when a new connection is received). The MFN selector 540 receives data used to determine which MFN to select for a new connection from the ECN tracker 550. The MFN selector and ECN tracker 550 are further described with respect to FIG. 6, below. In alternate embodiments, some encapsulation and/or MFN determination functions may be performed by the gateways 525 and 530.



FIG. 6 illustrates MFN selection elements of some embodiments. In FIG. 6, first, the CFE 535 receives and de-encapsulates ACK packets from earlier data streams. One of ordinary skill in the art will understand that these earlier data streams do not have to have the same source or destination as a new data stream. Second, the CFE 535 sends the ECN values of the ACK packets to an ECN value tracker 610. In some embodiments, the CFE 535 sends, and the ECN value tracker 610 stores, the ECN values in association with an identifier of the MFN from which the packets were received. Third, the ECN values (and associated MFN identifiers) are then provided to or retrieved by a load percentage calculator 620. Fourth, the load percentages are stored in a load percentage value storage 630. In some embodiments, these first four operations go on in the background in order to prepare the data for later MFN selection.


Fifth, data packets of a new connection are received at the CFE 535. These packets may be received from a gateway (e.g., gateways 525 or 530 of FIG. 5) or through cloud routing fabric from another MFN (not shown). Sixth, the CFE 535, of FIG. 6, sends an MFN selection request to the MFN selector 540. Seventh, the load percentage values are retrieved from the load percentage value storage 630 by the MFN selector 540. Eighth, the MFN selector 540 sends the MFN selection to the CFE 535 (e.g., after calculating the RFC for each candidate MFN and selecting an MFN based at least partly on those calculations). Ninth, the CFE 535 encapsulates the data packets of the new data stream and sends them to the selected MFN. One of ordinary skill in the art will understand that FIG. 6 presents an example of an embodiment of a system for tracking load percentages and selecting MFNs for a new data stream, but that other systems which perform the same or similar functions, either separately or in combination, are used in other embodiments.


Although the above described embodiments use ACK packets as carriers of data concerning load percentages of servers on which MFNs with CFEs operate, one of ordinary skill in the art will understand that load percentage data can be carried in initial data packets instead of or in addition to being carried in ACK packets in some embodiments. Although the above description may refer to MFN selection, one of ordinary skill in the art will understand that in some embodiments, the selection is of a particular CFE or of a particular data center. Although the above described embodiments provided an example of calculations used to select a particular MFN, in some embodiments, one of ordinary skill in the art will understand that in other embodiments, other calculations may be used. Furthermore, in some embodiments, other considerations in addition to load percentage and/or resource use may influence the selection of an MFN. Although the above described embodiments determine a next hop MFN at each MFN along a path to a destination, in some embodiments, an entire path of hops is selected at a particular MFN (e.g., the first MFN to receive a new data stream). In the above described embodiments, each candidate MFN had previously sent ACK packets to an MFN determining a next hop. However, in some embodiments, one or more candidate MFN may not have previously sent packets to the first MFN. In such embodiments, the MFN selector may use other factors (e.g., a default load percentage, etc.) to determine whether to assign a new connection to the previously unknown candidate MFN.


This specification refers throughout to computational and network environments that include virtual machines (VMs). However, virtual machines are merely one example of data compute nodes (DCNs) or data compute end nodes, also referred to as addressable nodes. DCNs may include non-virtualized physical hosts, virtual machines, containers that run on top of a host operating system without the need for a hypervisor or separate operating system, and hypervisor kernel network interface modules.


VMs, in some embodiments, operate with their own guest operating systems on a host using resources of the host virtualized by virtualization software (e.g., a hypervisor, virtual machine monitor, etc.). The tenant (i.e., the owner of the VM) can choose which applications to operate on top of the guest operating system. Some containers, on the other hand, are constructs that run on top of a host operating system without the need for a hypervisor or separate guest operating system. In some embodiments, the host operating system uses name spaces to isolate the containers from each other and therefore provides operating-system level segregation of the different groups of applications that operate within different containers. This segregation is akin to the VM segregation that is offered in hypervisor-virtualized environments that virtualize system hardware, and thus can be viewed as a form of virtualization that isolates different groups of applications that operate in different containers. Such containers are more lightweight than VMs.


Hypervisor kernel network interface modules, in some embodiments, are non-VM DCNs that include a network stack with a hypervisor kernel network interface and receive/transmit threads. One example of a hypervisor kernel network interface module is the vmknic module that is part of the ESXi™ hypervisor of VMware, Inc.


It should be understood that while the specification refers to VMs, the examples given could be any type of DCNs, including physical hosts, VMs, non-VM containers, and hypervisor kernel network interface modules. In fact, the example networks could include combinations of different types of DCNs in some embodiments.


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


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



FIG. 7 conceptually illustrates a computer system 700 with which some embodiments of the invention are implemented. The computer system 700 can be used to implement any of the above-described hosts, controllers, gateway and edge forwarding elements. As such, it can be used to execute any of the above-described processes. This computer system 700 includes various types of non-transitory machine-readable media and interfaces for various other types of machine-readable media. Computer system 700 includes a bus 705, processing unit(s) 710, a system memory 725, a read-only memory 730, a permanent storage device 735, input devices 740, and output devices 745.


The bus 705 collectively represents all system, peripheral, and chipset buses that communicatively connect the numerous internal devices of the computer system 700. For instance, the bus 705 communicatively connects the processing unit(s) 710 with the read-only memory 730, the system memory 725, and the permanent storage device 735.


From these various memory units, the processing unit(s) 710 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) 730 stores static data and instructions that are needed by the processing unit(s) 710 and other modules of the computer system. The permanent storage device 735, 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 700 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 735.


Other embodiments use a removable storage device (such as a floppy disk, flash drive, etc.) as the permanent storage device 735. Like the permanent storage device 735, the system memory 725 is a read-and-write memory device. However, unlike storage device 735, the system memory 725 is a volatile read-and-write memory, such as random access memory. The system memory 725 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 725, the permanent storage device 735, and/or the read-only memory 730. From these various memory units, the processing unit(s) 710 retrieve instructions to execute and data to process in order to execute the processes of some embodiments.


The bus 705 also connects to the input and output devices 740 and 745. The input devices 740 enable the user to communicate information and select commands to the computer system 700. The input devices 740 include alphanumeric keyboards and pointing devices (also called “cursor control devices”). The output devices 745 display images generated by the computer system 700. The output devices 745 include printers and display devices, such as cathode ray tubes (CRT) or liquid crystal displays (LCD). Some embodiments include devices such as touchscreens that function as both input and output devices 740 and 745.


Finally, as shown in FIG. 7, bus 705 also couples computer system 700 to a network 765 through a network adapter (not shown). In this manner, the computer 700 can be a part of a network of computers (such as a local area network (“LAN”), a wide area network (“WAN”), or an Intranet), or a network of networks (such as the Internet). Any or all components of computer system 700 may be used in conjunction with the invention.


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


While the above discussion primarily refers to microprocessors 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” mean 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 embodiments deploy CFEs in public cloud datacenters. However, in other embodiments, the CFEs are deployed in a third-party's private cloud datacenters (e.g., datacenters that the third-party uses to deploy cloud CFEs for different entities in order to deploy virtual networks for these entities). Thus, one of ordinary skill in the art would understand that the invention is not to be limited by the foregoing illustrative details, but rather is to be defined by the appended claims.

Claims
  • 1. A method of reducing network congestion in a virtual network, the method comprising, at a first cloud forwarding element (CFE) of the virtual network: receiving a plurality of encapsulated data packets of a data stream, the encapsulated data packets having been encapsulated by a second CFE, operating on a server of the virtual network, wherein the second CFE: identifies a load percentage of the server;sets explicit congestion notification (ECN) bits on a percentage of non-encapsulated data packets based on the load percentage of the server; andencapsulates each non-encapsulated data packet; anddetermining whether to forward a new connection to the second CFE based at least on the percentage of data packets received from the second CFE with the ECN bits set.
  • 2. The method of claim 1, wherein the load percentage comprises a maximum of (i) a percentage of CPU resources of the server in use and (ii) a percentage of memory resources of the server in use.
  • 3. The method of claim 1, wherein setting the ECN bits on a percentage of the data packets based on the load percentage comprises, for each data packet, randomly determining whether to set an ECN bit of the data packet to a positive status or a negative status with a probability to set the ECN bit to a positive status being based on the load percentage.
  • 4. The method of claim 3, wherein the probability to set the ECN bit to a positive status is equal to the load percentage.
  • 5. The method of claim 1, wherein: the second CFE is one of a set of CFEs able to process data packets of the new connection;each CFE of the set of CFEs sends data packets to the first CFE with a percentage of the data packets having an ECN bit set; anddetermining whether to forward the new connection to the second CFE comprises comparing the percentage of data packets from the second CFE with the ECN bits set to percentages of data packets from each of the CFEs in the set of CFEs with the ECN bits set.
  • 6. The method of claim 5, wherein comparing the percentage of data packets from the second CFE with the ECN bits set to percentages of data packets from each of the CFEs in the set of CFEs with the ECN bits set comprises calculating a relative free capacity of the second CFE by calculating a free capacity for each CFE in the set of CFEs and dividing the free capacity of the second CFE by the sum of the free capacities of the CFEs in the set of CFEs.
  • 7. The method of claim 6, wherein determining whether to forward the new connection to the second CFE comprises randomly assigning the new connection to the second CFE or to another CFE of the set of CFEs with the probability of assigning the new connection to the second CFE being based on the relative free capacity of the second CFE.
  • 8. The method of claim 1, wherein the ECN bits of an encapsulated data packet comprise a first encapsulated set of ECN bits and the encapsulated data packet further comprises a second, non-encapsulated set of ECN bits.
  • 9. The method of claim 8, wherein a hardware routing system underlies the virtual network, the second, non-encapsulated set of ECN bits is identifiable by the hardware routing system, and the first, encapsulated set of ECN bits is not identifiable by the hardware routing system.
  • 10. The method of claim 1, wherein the plurality of encapsulated data packets is a plurality of encapsulated acknowledgement (ACK) packets each sent in response to an encapsulated packet received by the second CFE from the first CFE.
  • 11. A method of reducing network congestion in a virtual network, the method comprising, at a first cloud forwarding element (CFE) of the virtual network: receiving a plurality of encapsulated data packets of a data stream, the encapsulated data packets having been encapsulated by a second CFE, operating on a server of the virtual network, wherein the second CFE: identifies a memory usage percentage and a CPU usage percentage of the server;sets first explicit congestion notification (ECN) bits on a percentage of non-encapsulated data packets based on the memory usage percentage and sets second ECN bits of a percentage of the non-encapsulated data packets based on the CPU usage percentage; andencapsulates each non-encapsulated data packet to generate an encapsulated data packet; anddetermining whether to forward a new connection to the second CFE based on at least one of the percentage of data packets from the second CFE with the first ECN bits set and the percentage of data packets from the second CFE with the second ECN bits set.
  • 12. The method of claim 11, wherein determining whether to forward the new connection to the second CFE is based on both the percentage of data packets from the second CFE with the first ECN bits set and the percentage of data packets from the second CFE with the second ECN bits set.
  • 13. The method of claim 11, wherein the memory percentage of the server comprises a percentage of memory resources of the server that are allocated to the second CFE and in use handling data packets and the CPU usage percentage of the server comprises a percentage of CPU resources of the server that are allocated to the second CFE and in use handling data packets.
  • 14. The method of claim 11, wherein the plurality of encapsulated data packets of the data stream is a first plurality of encapsulated packets of a first data stream and the server is a first server of the virtual network, the method further comprising: receiving a second plurality of encapsulated data packets of a second data stream, the encapsulated data packets of the second plurality of encapsulated data packets having been encapsulated by a third CFE, operating on a second server of the virtual network, wherein the third CFE: identifies a memory usage percentage and a CPU usage percentage of the second server;sets first explicit congestion notification (ECN) bits of a percentage of non-encapsulated data packets of the second server based on the memory usage percentage of the second server and sets second explicit congestion notification bits of a percentage of the non-encapsulated data packets of the second server based on the CPU usage percentage of the second server; andencapsulates each non-encapsulated data packet of the second server to generate an encapsulated data packet of the second plurality of encapsulated data packets; andwherein determining whether to forward the new connection to the second CFE comprises selecting between the second CFE and the third CFE based on (i) at least one of the percentage of data packets from the second CFE with the first ECN bits set and the percentage of data packets from the second CFE with the second ECN bits set, and (ii) at least one of the percentage of data packets from the third CFE with the first ECN bits set and the percentage of data packets from the third CFE with the second ECN bits set.
  • 15. A machine readable medium storing a program which when executed by one or more processing units reduces network congestion in a virtual network, the program comprising sets of instructions for, at a first cloud forwarding element (CFE) of the virtual network: receiving a plurality of encapsulated data packets of a data stream, the encapsulated data packets having been encapsulated by a second CFE, operating on a server of the virtual network, wherein the second CFE: identifies a load percentage of the server;sets explicit congestion notification (ECN) bits on a percentage of non-encapsulated data packets based on the load percentage of the server; andencapsulates each non-encapsulated data packet; anddetermining whether to forward a new connection to the second CFE based at least on the percentage of data packets received from the second CFE with the ECN bits set.
  • 16. The machine readable medium of claim 15, wherein the load percentage comprises a maximum of (i) a percentage of CPU resources of the server in use and (ii) a percentage of memory resources of the server in use.
  • 17. The machine readable medium of claim 15, wherein setting the ECN bits on a percentage of the data packets based on the load percentage comprises, for each data packet, randomly determining whether to set an ECN bit of the data packet to a positive status or a negative status with a probability to set the ECN bit to a positive status being based on the load percentage.
  • 18. The machine readable medium of claim 17, wherein the probability to set the ECN bit to a positive status is equal to the load percentage.
  • 19. The machine readable medium of claim 15, wherein: the second CFE is one of a set of CFEs able to process data packets of the new connection;each CFE of the set of CFEs sends data packets to the first CFE with a percentage of the data packets having an ECN bit set; anddetermining whether to forward the new connection to the second CFE comprises comparing the percentage of data packets from the second CFE with the ECN bits set to percentages of data packets from each of the CFEs in the set of CFEs with the ECN bits set.
  • 20. The machine readable medium of claim 19, wherein comparing the percentage of data packets from the second CFE with the ECN bits set to percentages of data packets from each of the CFEs in the set of CFEs with the ECN bits set comprises calculating a relative free capacity of the second CFE by calculating a free capacity for each CFE in the set of CFEs and dividing the free capacity of the second CFE by the sum of the free capacities of the CFEs in the set of CFEs.
US Referenced Citations (1041)
Number Name Date Kind
5652751 Sharony Jul 1997 A
5909553 Campbell et al. Jun 1999 A
6154465 Pickett Nov 2000 A
6157648 Voit et al. Dec 2000 A
6201810 Masuda et al. Mar 2001 B1
6363378 Conklin et al. Mar 2002 B1
6445682 Weitz Sep 2002 B1
6744775 Beshai et al. Jun 2004 B1
6976087 Westfall et al. Dec 2005 B1
7003481 Banka et al. Feb 2006 B2
7280476 Anderson Oct 2007 B2
7313629 Nucci et al. Dec 2007 B1
7320017 Kurapati et al. Jan 2008 B1
7373660 Guichard et al. May 2008 B1
7581022 Griffin et al. Aug 2009 B1
7680925 Sathyanarayana et al. Mar 2010 B2
7681236 Tamura et al. Mar 2010 B2
7751409 Carolan Jul 2010 B1
7962458 Holenstein et al. Jun 2011 B2
8051185 Lee et al. Nov 2011 B2
8094575 Vadlakonda et al. Jan 2012 B1
8094659 Arad Jan 2012 B1
8111692 Ray Feb 2012 B2
8141156 Mao et al. Mar 2012 B1
8224971 Miller et al. Jul 2012 B1
8228928 Parandekar et al. Jul 2012 B2
8243589 Trost et al. Aug 2012 B1
8259566 Chen et al. Sep 2012 B2
8274891 Averi et al. Sep 2012 B2
8301749 Finklestein et al. Oct 2012 B1
8385227 Downey Feb 2013 B1
8516129 Skene Aug 2013 B1
8566452 Goodwin, III et al. Oct 2013 B1
8588066 Goel et al. Nov 2013 B2
8630291 Shaffer et al. Jan 2014 B2
8661295 Khanna et al. Feb 2014 B1
8724456 Hong et al. May 2014 B1
8724503 Johnsson et al. May 2014 B2
8745177 Kazerani et al. Jun 2014 B1
8769129 Watsen et al. Jul 2014 B2
8797874 Yu et al. Aug 2014 B2
8799504 Capone et al. Aug 2014 B2
8804745 Sinn Aug 2014 B1
8806482 Nagargadde et al. Aug 2014 B1
8855071 Sankaran et al. Oct 2014 B1
8856339 Mestery et al. Oct 2014 B2
8964548 Keralapura et al. Feb 2015 B1
8989199 Sella et al. Mar 2015 B1
9009217 Nagargadde et al. Apr 2015 B1
9015299 Shah Apr 2015 B1
9019837 Lue et al. Apr 2015 B2
9055000 Ghosh et al. Jun 2015 B1
9060025 Xu Jun 2015 B2
9071607 Twitchell, Jr. Jun 2015 B2
9075771 Gawali et al. Jul 2015 B1
9100329 Jiang et al. Aug 2015 B1
9135037 Petrescu-Prahova et al. Sep 2015 B1
9137334 Zhou Sep 2015 B2
9154327 Marino et al. Oct 2015 B1
9203764 Shirazipour et al. Dec 2015 B2
9225591 Beheshti-Zavareh et al. Dec 2015 B2
9306949 Richard et al. Apr 2016 B1
9323561 Ayala et al. Apr 2016 B2
9336040 Dong et al. May 2016 B2
9354983 Yenamandra et al. May 2016 B1
9356943 Lopilato et al. May 2016 B1
9379981 Zhou et al. Jun 2016 B1
9413724 Xu Aug 2016 B2
9419878 Hsiao et al. Aug 2016 B2
9432245 Sorenson et al. Aug 2016 B1
9438566 Zhang et al. Sep 2016 B2
9450817 Bahadur et al. Sep 2016 B1
9450852 Chen et al. Sep 2016 B1
9462010 Stevenson Oct 2016 B1
9467478 Khan et al. Oct 2016 B1
9485163 Fries et al. Nov 2016 B1
9521067 Michael et al. Dec 2016 B2
9525564 Lee Dec 2016 B2
9542219 Bryant et al. Jan 2017 B1
9548930 Hardie Jan 2017 B1
9559951 Sajassi et al. Jan 2017 B1
9563423 Pittman Feb 2017 B1
9602389 Maveli et al. Mar 2017 B1
9608917 Anderson et al. Mar 2017 B1
9608962 Chang Mar 2017 B1
9614748 Battersby et al. Apr 2017 B1
9621460 Mehta et al. Apr 2017 B2
9641551 Kariyanahalli May 2017 B1
9648547 Hart et al. May 2017 B1
9665432 Kruse et al. May 2017 B2
9686127 Ramachandran et al. Jun 2017 B2
9692714 Nair et al. Jun 2017 B1
9715401 Devine et al. Jul 2017 B2
9717021 Hughes et al. Jul 2017 B2
9722815 Mukundan et al. Aug 2017 B2
9747249 Cherian et al. Aug 2017 B2
9755965 Yadav et al. Sep 2017 B1
9787559 Schroeder Oct 2017 B1
9807004 Koley et al. Oct 2017 B2
9819540 Bahadur et al. Nov 2017 B1
9819565 Djukic et al. Nov 2017 B2
9825822 Holland Nov 2017 B1
9825911 Brandwine Nov 2017 B1
9825992 Xu Nov 2017 B2
9832128 Ashner et al. Nov 2017 B1
9832205 Santhi et al. Nov 2017 B2
9875355 Williams Jan 2018 B1
9906401 Rao Feb 2018 B1
9923826 Murgia Mar 2018 B2
9930011 Clemons, Jr. et al. Mar 2018 B1
9935829 Miller et al. Apr 2018 B1
9942787 Tillotson Apr 2018 B1
9996370 Khafizov et al. Jun 2018 B1
10038601 Becker et al. Jul 2018 B1
10057183 Salle et al. Aug 2018 B2
10057294 Xu Aug 2018 B2
10116593 Sinn et al. Oct 2018 B1
10135789 Mayya et al. Nov 2018 B2
10142226 Wu et al. Nov 2018 B1
10178032 Freitas Jan 2019 B1
10178037 Appleby et al. Jan 2019 B2
10187289 Chen et al. Jan 2019 B1
10200264 Menon et al. Feb 2019 B2
10229017 Zou et al. Mar 2019 B1
10237123 Dubey et al. Mar 2019 B2
10250498 Bales et al. Apr 2019 B1
10263832 Ghosh Apr 2019 B1
10263848 Wolting Apr 2019 B2
10320664 Nainar et al. Jun 2019 B2
10320691 Matthews et al. Jun 2019 B1
10326830 Singh Jun 2019 B1
10348767 Lee et al. Jul 2019 B1
10355989 Panchal et al. Jul 2019 B1
10425382 Mayya et al. Sep 2019 B2
10454708 Mibu Oct 2019 B2
10454714 Mayya et al. Oct 2019 B2
10461993 Turabi et al. Oct 2019 B2
10498652 Mayya et al. Dec 2019 B2
10511546 Singarayan et al. Dec 2019 B2
10523539 Mayya et al. Dec 2019 B2
10550093 Ojima et al. Feb 2020 B2
10554538 Spohn et al. Feb 2020 B2
10560431 Chen et al. Feb 2020 B1
10565464 Han et al. Feb 2020 B2
10567519 Mukhopadhyaya et al. Feb 2020 B1
10574482 Oréet al. Feb 2020 B2
10574528 Mayya et al. Feb 2020 B2
10594516 Cidon et al. Mar 2020 B2
10594591 Houjyo et al. Mar 2020 B2
10594659 El-Moussa et al. Mar 2020 B2
10608844 Cidon et al. Mar 2020 B2
10630505 Rubenstein et al. Apr 2020 B2
10637889 Ermagan et al. Apr 2020 B2
10666460 Cidon et al. May 2020 B2
10666497 Tahhan et al. May 2020 B2
10686625 Cidon et al. Jun 2020 B2
10693739 Naseri et al. Jun 2020 B1
10708144 Mohan et al. Jul 2020 B2
10715382 Guan et al. Jul 2020 B2
10715427 Raj et al. Jul 2020 B2
10749711 Mukundan et al. Aug 2020 B2
10778466 Cidon et al. Sep 2020 B2
10778528 Mayya et al. Sep 2020 B2
10778557 Ganichev et al. Sep 2020 B2
10805114 Cidon et al. Oct 2020 B2
10805272 Mayya et al. Oct 2020 B2
10819564 Turabi et al. Oct 2020 B2
10826775 Moreno et al. Nov 2020 B1
10841131 Cidon et al. Nov 2020 B2
10911374 Kumar et al. Feb 2021 B1
10924388 Burns et al. Feb 2021 B1
10938693 Mayya et al. Mar 2021 B2
10951529 Duan et al. Mar 2021 B2
10958479 Cidon et al. Mar 2021 B2
10959098 Cidon et al. Mar 2021 B2
10992558 Silva et al. Apr 2021 B1
10992568 Michael et al. Apr 2021 B2
10999100 Cidon et al. May 2021 B2
10999137 Cidon et al. May 2021 B2
10999165 Cidon et al. May 2021 B2
10999197 Hooda et al. May 2021 B2
11005684 Cidon May 2021 B2
11018995 Cidon et al. May 2021 B2
11044190 Ramaswamy et al. Jun 2021 B2
11050588 Mayya et al. Jun 2021 B2
11050644 Hegde et al. Jun 2021 B2
11071005 Shen et al. Jul 2021 B2
11089111 Markuze et al. Aug 2021 B2
11095612 Oswal et al. Aug 2021 B1
11102032 Cidon et al. Aug 2021 B2
11108595 Knutsen et al. Aug 2021 B2
11108851 Kurmala et al. Aug 2021 B1
11115347 Gupta et al. Sep 2021 B2
11115426 Pazhyannur et al. Sep 2021 B1
11115480 Markuze et al. Sep 2021 B2
11121962 Michael et al. Sep 2021 B2
11121985 Cidon et al. Sep 2021 B2
11128492 Sethi et al. Sep 2021 B2
11146632 Rubenstein Oct 2021 B2
11153230 Cidon et al. Oct 2021 B2
11171885 Cidon et al. Nov 2021 B2
11212140 Mukundan et al. Dec 2021 B2
11212238 Cidon et al. Dec 2021 B2
11223514 Mayya et al. Jan 2022 B2
11245641 Ramaswamy et al. Feb 2022 B2
11252079 Michael et al. Feb 2022 B2
11252105 Cidon et al. Feb 2022 B2
11252106 Cidon et al. Feb 2022 B2
11258728 Cidon et al. Feb 2022 B2
11310170 Cidon et al. Apr 2022 B2
11323307 Mayya et al. May 2022 B2
11349722 Mayya et al. May 2022 B2
11363124 Markuze et al. Jun 2022 B2
11374904 Mayya et al. Jun 2022 B2
11375005 Rolando et al. Jun 2022 B1
11381474 Kumar et al. Jul 2022 B1
11381499 Ramaswamy et al. Jul 2022 B1
11388086 Ramaswamy et al. Jul 2022 B1
11394640 Ramaswamy et al. Jul 2022 B2
11418997 Devadoss et al. Aug 2022 B2
11438789 Devadoss et al. Sep 2022 B2
11444865 Ramaswamy et al. Sep 2022 B2
11444872 Mayya et al. Sep 2022 B2
11477127 Ramaswamy et al. Oct 2022 B2
11489720 Kempanna et al. Nov 2022 B1
11489783 Ramaswamy et al. Nov 2022 B2
11509571 Ramaswamy et al. Nov 2022 B1
11516049 Cidon et al. Nov 2022 B2
11522780 Wallace et al. Dec 2022 B1
11526434 Brooker et al. Dec 2022 B1
11533248 Mayya et al. Dec 2022 B2
11552874 Pragada et al. Jan 2023 B1
11575591 Ramaswamy et al. Feb 2023 B2
11575600 Markuze et al. Feb 2023 B2
11582144 Ramaswamy et al. Feb 2023 B2
11582298 Hood et al. Feb 2023 B2
11601356 Gandhi et al. Mar 2023 B2
11606225 Cidon et al. Mar 2023 B2
11606286 Michael et al. Mar 2023 B2
11606314 Cidon et al. Mar 2023 B2
11606712 Devadoss et al. Mar 2023 B2
11611507 Ramaswamy et al. Mar 2023 B2
11637768 Ramaswamy et al. Apr 2023 B2
11677720 Mayya et al. Jun 2023 B2
11689959 Devadoss et al. Jun 2023 B2
11700196 Michael et al. Jul 2023 B2
11706126 Silva et al. Jul 2023 B2
11706127 Michael et al. Jul 2023 B2
11709710 Markuze et al. Jul 2023 B2
11716286 Ramaswamy et al. Aug 2023 B2
11722925 Devadoss et al. Aug 2023 B2
11729065 Ramaswamy et al. Aug 2023 B2
20020049687 Helsper et al. Apr 2002 A1
20020075542 Kumar et al. Jun 2002 A1
20020085488 Kobayashi Jul 2002 A1
20020087716 Mustafa Jul 2002 A1
20020152306 Tuck Oct 2002 A1
20020186682 Kawano et al. Dec 2002 A1
20020198840 Banka et al. Dec 2002 A1
20030050061 Wu et al. Mar 2003 A1
20030061269 Hathaway et al. Mar 2003 A1
20030088697 Matsuhira May 2003 A1
20030112766 Riedel et al. Jun 2003 A1
20030112808 Solomon Jun 2003 A1
20030126468 Markham Jul 2003 A1
20030161313 Jinmei et al. Aug 2003 A1
20030161321 Karam et al. Aug 2003 A1
20030189919 Gupta et al. Oct 2003 A1
20030202506 Perkins et al. Oct 2003 A1
20030219030 Gubbi Nov 2003 A1
20040052212 Baillargeon Mar 2004 A1
20040059831 Chu et al. Mar 2004 A1
20040068668 Lor et al. Apr 2004 A1
20040165601 Liu et al. Aug 2004 A1
20040224771 Chen et al. Nov 2004 A1
20050078690 DeLangis Apr 2005 A1
20050149604 Navada Jul 2005 A1
20050154790 Nagata et al. Jul 2005 A1
20050172161 Cruz et al. Aug 2005 A1
20050195754 Nosella Sep 2005 A1
20050210479 Andjelic Sep 2005 A1
20050265255 Kodialam et al. Dec 2005 A1
20060002291 Alicherry et al. Jan 2006 A1
20060034335 Karaoguz et al. Feb 2006 A1
20060114838 Mandavilli et al. Jun 2006 A1
20060171365 Borella Aug 2006 A1
20060182034 Klinker et al. Aug 2006 A1
20060182035 Vasseur Aug 2006 A1
20060193247 Naseh et al. Aug 2006 A1
20060193252 Naseh et al. Aug 2006 A1
20060195605 Sundarrajan et al. Aug 2006 A1
20060245414 Susai et al. Nov 2006 A1
20070050594 Augsburg et al. Mar 2007 A1
20070064604 Chen et al. Mar 2007 A1
20070064702 Bates et al. Mar 2007 A1
20070083727 Johnston et al. Apr 2007 A1
20070091794 Filsfils et al. Apr 2007 A1
20070103548 Carter May 2007 A1
20070115812 Hughes May 2007 A1
20070121486 Guichard et al. May 2007 A1
20070130325 Lesser Jun 2007 A1
20070162619 Aloni et al. Jul 2007 A1
20070162639 Chu et al. Jul 2007 A1
20070177511 Das et al. Aug 2007 A1
20070195797 Patel et al. Aug 2007 A1
20070237081 Kodialam et al. Oct 2007 A1
20070260746 Mirtorabi et al. Nov 2007 A1
20070268882 Breslau et al. Nov 2007 A1
20080002670 Bugenhagen et al. Jan 2008 A1
20080049621 McGuire et al. Feb 2008 A1
20080055241 Goldenberg et al. Mar 2008 A1
20080080509 Khanna et al. Apr 2008 A1
20080095187 Jung et al. Apr 2008 A1
20080117930 Chakareski et al. May 2008 A1
20080144532 Chamarajanagar et al. Jun 2008 A1
20080168086 Miller et al. Jul 2008 A1
20080175150 Bolt et al. Jul 2008 A1
20080181116 Kavanaugh et al. Jul 2008 A1
20080219276 Shah Sep 2008 A1
20080240121 Xiong et al. Oct 2008 A1
20080263218 Beerends et al. Oct 2008 A1
20090013210 McIntosh et al. Jan 2009 A1
20090028092 Rothschild Jan 2009 A1
20090125617 Klessig et al. May 2009 A1
20090141642 Sun Jun 2009 A1
20090154463 Hines et al. Jun 2009 A1
20090182874 Morford et al. Jul 2009 A1
20090247204 Sennett et al. Oct 2009 A1
20090268605 Campbell et al. Oct 2009 A1
20090274045 Meier et al. Nov 2009 A1
20090276657 Wetmore et al. Nov 2009 A1
20090303880 Maltz et al. Dec 2009 A1
20100008361 Guichard et al. Jan 2010 A1
20100017802 Lojewski Jan 2010 A1
20100046532 Okita Feb 2010 A1
20100061379 Parandekar et al. Mar 2010 A1
20100080129 Strahan et al. Apr 2010 A1
20100088440 Banks et al. Apr 2010 A1
20100091782 Hiscock Apr 2010 A1
20100091823 Retana et al. Apr 2010 A1
20100098092 Luo et al. Apr 2010 A1
20100100768 Yamamoto et al. Apr 2010 A1
20100107162 Edwards et al. Apr 2010 A1
20100118727 Draves et al. May 2010 A1
20100118886 Saavedra May 2010 A1
20100128600 Srinivasmurthy et al. May 2010 A1
20100142539 Gooch Jun 2010 A1
20100165985 Sharma et al. Jul 2010 A1
20100191884 Holenstein et al. Jul 2010 A1
20100223621 Joshi et al. Sep 2010 A1
20100226246 Proulx Sep 2010 A1
20100290422 Haigh et al. Nov 2010 A1
20100309841 Conte Dec 2010 A1
20100309912 Mehta et al. Dec 2010 A1
20100322255 Hao et al. Dec 2010 A1
20100332657 Elyashev et al. Dec 2010 A1
20110001604 Ludlow et al. Jan 2011 A1
20110007752 Silva et al. Jan 2011 A1
20110032939 Nozaki et al. Feb 2011 A1
20110035187 DeJori et al. Feb 2011 A1
20110040814 Higgins Feb 2011 A1
20110075674 Li et al. Mar 2011 A1
20110078783 Duan et al. Mar 2011 A1
20110107139 Middlecamp et al. May 2011 A1
20110110370 Moreno et al. May 2011 A1
20110141877 Xu et al. Jun 2011 A1
20110142041 Imai Jun 2011 A1
20110153909 Dong Jun 2011 A1
20110235509 Szymanski Sep 2011 A1
20110255397 Kadakia et al. Oct 2011 A1
20110302663 Prodan et al. Dec 2011 A1
20120008630 Ould-Brahim Jan 2012 A1
20120027013 Napierala Feb 2012 A1
20120039309 Evans et al. Feb 2012 A1
20120099601 Haddad et al. Apr 2012 A1
20120136697 Peles et al. May 2012 A1
20120140935 Kruglick Jun 2012 A1
20120147750 Pelletier Jun 2012 A1
20120157068 Eichen et al. Jun 2012 A1
20120173694 Yan et al. Jul 2012 A1
20120173919 Patel et al. Jul 2012 A1
20120182940 Taleb et al. Jul 2012 A1
20120221955 Raleigh et al. Aug 2012 A1
20120227093 Shatzkamer et al. Sep 2012 A1
20120240185 Kapoor et al. Sep 2012 A1
20120250682 Vincent et al. Oct 2012 A1
20120250686 Vincent et al. Oct 2012 A1
20120266026 Chikkalingaiah et al. Oct 2012 A1
20120281706 Agarwal et al. Nov 2012 A1
20120287818 Corti et al. Nov 2012 A1
20120300615 Kempf et al. Nov 2012 A1
20120307659 Yamada Dec 2012 A1
20120317270 Vrbaski et al. Dec 2012 A1
20120317291 Wolfe Dec 2012 A1
20130007505 Spear Jan 2013 A1
20130019005 Hui et al. Jan 2013 A1
20130021968 Reznik et al. Jan 2013 A1
20130044764 Casado et al. Feb 2013 A1
20130051237 Ong Feb 2013 A1
20130051399 Zhang et al. Feb 2013 A1
20130054763 Merwe et al. Feb 2013 A1
20130086267 Gelenbe et al. Apr 2013 A1
20130097304 Asthana et al. Apr 2013 A1
20130103729 Cooney et al. Apr 2013 A1
20130103834 Dzerve et al. Apr 2013 A1
20130117530 Kim et al. May 2013 A1
20130124718 Griffith et al. May 2013 A1
20130124911 Griffith et al. May 2013 A1
20130124912 Griffith et al. May 2013 A1
20130128757 Chowdhary et al. May 2013 A1
20130128889 Mathur et al. May 2013 A1
20130142201 Kim et al. Jun 2013 A1
20130170354 Takashima et al. Jul 2013 A1
20130173768 Kundu et al. Jul 2013 A1
20130173788 Song Jul 2013 A1
20130182712 Aguayo et al. Jul 2013 A1
20130185446 Zeng et al. Jul 2013 A1
20130185729 Vasic et al. Jul 2013 A1
20130191688 Agarwal et al. Jul 2013 A1
20130223226 Narayanan et al. Aug 2013 A1
20130223454 Dunbar et al. Aug 2013 A1
20130235870 Tripathi et al. Sep 2013 A1
20130238782 Zhao et al. Sep 2013 A1
20130242718 Zhang Sep 2013 A1
20130254599 Katkar et al. Sep 2013 A1
20130258839 Wang et al. Oct 2013 A1
20130258847 Zhang et al. Oct 2013 A1
20130258939 Wang Oct 2013 A1
20130266015 Qu et al. Oct 2013 A1
20130266019 Qu et al. Oct 2013 A1
20130283364 Chang et al. Oct 2013 A1
20130286846 Atlas et al. Oct 2013 A1
20130297611 Moritz et al. Nov 2013 A1
20130297770 Zhang Nov 2013 A1
20130301469 Suga Nov 2013 A1
20130301642 Radhakrishnan et al. Nov 2013 A1
20130308444 Sem-Jacobsen et al. Nov 2013 A1
20130315242 Wang et al. Nov 2013 A1
20130315243 Huang et al. Nov 2013 A1
20130329548 Nakil et al. Dec 2013 A1
20130329601 Yin et al. Dec 2013 A1
20130329734 Chesla et al. Dec 2013 A1
20130346470 Obstfeld et al. Dec 2013 A1
20140016464 Shirazipour et al. Jan 2014 A1
20140019604 Twitchell, Jr. Jan 2014 A1
20140019750 Dodgson et al. Jan 2014 A1
20140040975 Raleigh et al. Feb 2014 A1
20140064283 Balus et al. Mar 2014 A1
20140071832 Johnsson et al. Mar 2014 A1
20140092907 Sridhar et al. Apr 2014 A1
20140108665 Arora et al. Apr 2014 A1
20140112171 Pasdar Apr 2014 A1
20140115584 Mudigonda et al. Apr 2014 A1
20140122559 Branson et al. May 2014 A1
20140123135 Huang et al. May 2014 A1
20140126418 Brendel et al. May 2014 A1
20140156818 Hunt Jun 2014 A1
20140156823 Liu et al. Jun 2014 A1
20140157363 Banerjee Jun 2014 A1
20140160935 Zecharia et al. Jun 2014 A1
20140164560 Ko et al. Jun 2014 A1
20140164617 Jalan et al. Jun 2014 A1
20140164718 Schaik et al. Jun 2014 A1
20140173113 Vemuri et al. Jun 2014 A1
20140173331 Martin et al. Jun 2014 A1
20140181824 Saund et al. Jun 2014 A1
20140189074 Parker Jul 2014 A1
20140208317 Nakagawa Jul 2014 A1
20140219135 Li et al. Aug 2014 A1
20140223507 Xu Aug 2014 A1
20140226664 Chen et al. Aug 2014 A1
20140229210 Sharifian et al. Aug 2014 A1
20140244851 Lee Aug 2014 A1
20140258535 Zhang Sep 2014 A1
20140269690 Tu Sep 2014 A1
20140279862 Dietz et al. Sep 2014 A1
20140280499 Basavaiah et al. Sep 2014 A1
20140310282 Sprague et al. Oct 2014 A1
20140317440 Biermayr et al. Oct 2014 A1
20140321277 Lynn, Jr. et al. Oct 2014 A1
20140337500 Lee Nov 2014 A1
20140337674 Ivancic et al. Nov 2014 A1
20140341109 Cartmell et al. Nov 2014 A1
20140351394 Elisha Nov 2014 A1
20140355441 Jain Dec 2014 A1
20140365834 Stone et al. Dec 2014 A1
20140372582 Ghanwani et al. Dec 2014 A1
20150003240 Drwiega et al. Jan 2015 A1
20150016249 Mukundan et al. Jan 2015 A1
20150029864 Raileanu et al. Jan 2015 A1
20150039744 Niazi et al. Feb 2015 A1
20150046572 Cheng et al. Feb 2015 A1
20150052247 Threefoot et al. Feb 2015 A1
20150052517 Raghu et al. Feb 2015 A1
20150056960 Egner et al. Feb 2015 A1
20150058917 Xu Feb 2015 A1
20150088942 Shah Mar 2015 A1
20150089628 Lang Mar 2015 A1
20150092603 Aguayo et al. Apr 2015 A1
20150096011 Watt Apr 2015 A1
20150100958 Banavalikar et al. Apr 2015 A1
20150106809 Reddy et al. Apr 2015 A1
20150124603 Ketheesan et al. May 2015 A1
20150134777 Onoue May 2015 A1
20150139238 Pourzandi et al. May 2015 A1
20150146539 Mehta et al. May 2015 A1
20150163152 Li Jun 2015 A1
20150169340 Haddad et al. Jun 2015 A1
20150172121 Farkas et al. Jun 2015 A1
20150172169 DeCusatis et al. Jun 2015 A1
20150188823 Williams et al. Jul 2015 A1
20150189009 Bemmel Jul 2015 A1
20150195178 Bhattacharya et al. Jul 2015 A1
20150201036 Nishiki et al. Jul 2015 A1
20150222543 Song Aug 2015 A1
20150222638 Morley Aug 2015 A1
20150236945 Michael et al. Aug 2015 A1
20150236962 Veres et al. Aug 2015 A1
20150244617 Nakil et al. Aug 2015 A1
20150249644 Xu Sep 2015 A1
20150257081 Ramanujan et al. Sep 2015 A1
20150264055 Budhani 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
20150326426 Luo et al. Nov 2015 A1
20150334025 Rader Nov 2015 A1
20150334696 Gu et al. Nov 2015 A1
20150341271 Gomez Nov 2015 A1
20150341273 Naouri Nov 2015 A1
20150349978 Wu et al. Dec 2015 A1
20150350907 Timariu et al. Dec 2015 A1
20150358232 Chen et al. Dec 2015 A1
20150358236 Roach et al. Dec 2015 A1
20150363221 Terayama et al. Dec 2015 A1
20150363733 Brown Dec 2015 A1
20150365323 Duminuco et al. Dec 2015 A1
20150372943 Hasan et al. Dec 2015 A1
20150372982 Herle et al. Dec 2015 A1
20150381407 Wang et al. Dec 2015 A1
20150381462 Choi et al. Dec 2015 A1
20150381493 Bansal et al. Dec 2015 A1
20160019317 Pawar et al. Jan 2016 A1
20160020844 Hart et al. Jan 2016 A1
20160021597 Hart et al. Jan 2016 A1
20160035183 Buchholz et al. Feb 2016 A1
20160036924 Koppolu et al. Feb 2016 A1
20160036938 Aviles et al. Feb 2016 A1
20160037434 Gopal et al. Feb 2016 A1
20160072669 Saavedra Mar 2016 A1
20160072684 Manuguri et al. Mar 2016 A1
20160080268 Anand et al. Mar 2016 A1
20160080502 Yadav et al. Mar 2016 A1
20160105353 Cociglio Apr 2016 A1
20160105392 Thakkar et al. Apr 2016 A1
20160105471 Nunes et al. Apr 2016 A1
20160105488 Thakkar et al. Apr 2016 A1
20160117185 Fang et al. Apr 2016 A1
20160134461 Sampath et al. May 2016 A1
20160134527 Kwak et al. May 2016 A1
20160134528 Lin et al. May 2016 A1
20160134591 Liao et al. May 2016 A1
20160142373 Ossipov May 2016 A1
20160147607 Dornemann et al. May 2016 A1
20160150055 Choi May 2016 A1
20160164832 Bellagamba et al. Jun 2016 A1
20160164914 Madhav et al. Jun 2016 A1
20160173338 Wolting Jun 2016 A1
20160191363 Haraszti et al. Jun 2016 A1
20160191374 Singh et al. Jun 2016 A1
20160192403 Gupta et al. Jun 2016 A1
20160197834 Luft Jul 2016 A1
20160197835 Luft Jul 2016 A1
20160198003 Luft Jul 2016 A1
20160205071 Cooper et al. Jul 2016 A1
20160210209 Verkaik et al. Jul 2016 A1
20160212773 Kanderholm et al. Jul 2016 A1
20160218947 Hughes et al. Jul 2016 A1
20160218951 Vasseur et al. Jul 2016 A1
20160234099 Jiao Aug 2016 A1
20160234161 Banerjee et al. Aug 2016 A1
20160255169 Kovvuri et al. Sep 2016 A1
20160255542 Hughes et al. Sep 2016 A1
20160261493 Li Sep 2016 A1
20160261495 Xia et al. Sep 2016 A1
20160261506 Hegde et al. Sep 2016 A1
20160261639 Xu Sep 2016 A1
20160269298 Li et al. Sep 2016 A1
20160269926 Sundaram Sep 2016 A1
20160285736 Gu Sep 2016 A1
20160299775 Madapurath et al. Oct 2016 A1
20160301471 Kunz et al. Oct 2016 A1
20160308762 Teng et al. Oct 2016 A1
20160315912 Mayya et al. Oct 2016 A1
20160323377 Einkauf et al. Nov 2016 A1
20160328159 Coddington et al. Nov 2016 A1
20160330111 Manghirmalani et al. Nov 2016 A1
20160337202 Ben-Itzhak et al. Nov 2016 A1
20160352588 Subbarayan et al. Dec 2016 A1
20160353268 Senarath et al. Dec 2016 A1
20160359738 Sullenberger et al. Dec 2016 A1
20160366187 Kamble Dec 2016 A1
20160371153 Dornemann Dec 2016 A1
20160378527 Zamir Dec 2016 A1
20160380886 Blair et al. Dec 2016 A1
20160380906 Hodique et al. Dec 2016 A1
20170005986 Bansal et al. Jan 2017 A1
20170006499 Hampel et al. Jan 2017 A1
20170012870 Blair et al. Jan 2017 A1
20170019428 Cohn Jan 2017 A1
20170024260 Chandrasekaran et al. Jan 2017 A1
20170026273 Yao et al. Jan 2017 A1
20170026283 Williams et al. Jan 2017 A1
20170026355 Mathaiyan et al. Jan 2017 A1
20170034046 Cai et al. Feb 2017 A1
20170034052 Chanda et al. Feb 2017 A1
20170034129 Sawant et al. Feb 2017 A1
20170048296 Ramalho et al. Feb 2017 A1
20170053258 Carney et al. Feb 2017 A1
20170055131 Kong et al. Feb 2017 A1
20170063674 Maskalik et al. Mar 2017 A1
20170063782 Jain et al. Mar 2017 A1
20170063783 Yong et al. Mar 2017 A1
20170063794 Jain et al. Mar 2017 A1
20170064005 Lee Mar 2017 A1
20170075710 Prasad et al. Mar 2017 A1
20170093625 Pera et al. Mar 2017 A1
20170097841 Chang et al. Apr 2017 A1
20170104653 Badea et al. Apr 2017 A1
20170104755 Arregoces et al. Apr 2017 A1
20170109212 Gaurav et al. Apr 2017 A1
20170118067 Vedula Apr 2017 A1
20170118173 Arramreddy et al. Apr 2017 A1
20170123939 Maheshwari et al. May 2017 A1
20170126475 Mahkonen et al. May 2017 A1
20170126516 Tiagi et al. May 2017 A1
20170126564 Mayya et al. May 2017 A1
20170134186 Mukundan et al. May 2017 A1
20170134520 Abbasi et al. May 2017 A1
20170139789 Fries et al. May 2017 A1
20170142000 Cai et al. May 2017 A1
20170149637 Banikazemi et al. May 2017 A1
20170155557 Desai et al. Jun 2017 A1
20170155566 Martinsen et al. Jun 2017 A1
20170155590 Dillon et al. Jun 2017 A1
20170163473 Sadana et al. Jun 2017 A1
20170171024 Anerousis et al. Jun 2017 A1
20170171310 Gardner Jun 2017 A1
20170180220 Leckey et al. Jun 2017 A1
20170181210 Nadella et al. Jun 2017 A1
20170195161 Ruel et al. Jul 2017 A1
20170195169 Mills et al. Jul 2017 A1
20170201568 Hussam et al. Jul 2017 A1
20170201585 Doraiswamy et al. Jul 2017 A1
20170207976 Rovner et al. Jul 2017 A1
20170214545 Cheng et al. Jul 2017 A1
20170214701 Hasan Jul 2017 A1
20170223117 Messerli et al. Aug 2017 A1
20170236060 Ignatyev Aug 2017 A1
20170237710 Mayya et al. Aug 2017 A1
20170242784 Heorhiadi et al. Aug 2017 A1
20170257260 Govindan et al. Sep 2017 A1
20170257309 Appanna Sep 2017 A1
20170264496 Ao et al. Sep 2017 A1
20170279717 Bethers et al. Sep 2017 A1
20170279741 Elias et al. Sep 2017 A1
20170279803 Desai et al. Sep 2017 A1
20170280474 Vesterinen et al. Sep 2017 A1
20170288987 Pasupathy et al. Oct 2017 A1
20170289002 Ganguli et al. Oct 2017 A1
20170289027 Ratnasingham Oct 2017 A1
20170295264 Touitou et al. Oct 2017 A1
20170302501 Shi et al. Oct 2017 A1
20170302565 Ghobadi et al. Oct 2017 A1
20170310641 Jiang et al. Oct 2017 A1
20170310691 Vasseur et al. Oct 2017 A1
20170317945 Guo et al. Nov 2017 A1
20170317954 Masurekar et al. Nov 2017 A1
20170317969 Masurekar et al. Nov 2017 A1
20170317974 Masurekar et al. Nov 2017 A1
20170324628 Dhanabalan Nov 2017 A1
20170337086 Zhu et al. Nov 2017 A1
20170339022 Hegde et al. Nov 2017 A1
20170339054 Yadav et al. Nov 2017 A1
20170339070 Chang et al. Nov 2017 A1
20170346722 Smith et al. Nov 2017 A1
20170364419 Lo Dec 2017 A1
20170366445 Nemirovsky et al. Dec 2017 A1
20170366467 Martin et al. Dec 2017 A1
20170373950 Szilagyi et al. Dec 2017 A1
20170374174 Evens et al. Dec 2017 A1
20180006995 Bickhart et al. Jan 2018 A1
20180007005 Chanda et al. Jan 2018 A1
20180007123 Cheng et al. Jan 2018 A1
20180013636 Seetharamaiah et al. Jan 2018 A1
20180014051 Phillips et al. Jan 2018 A1
20180020035 Boggia et al. Jan 2018 A1
20180034668 Mayya et al. Feb 2018 A1
20180041425 Zhang Feb 2018 A1
20180041470 Schultz et al. Feb 2018 A1
20180062875 Tumuluru Mar 2018 A1
20180062914 Boutros et al. Mar 2018 A1
20180062917 Chandrashekhar et al. Mar 2018 A1
20180063036 Chandrashekhar et al. Mar 2018 A1
20180063193 Chandrashekhar et al. Mar 2018 A1
20180063233 Park Mar 2018 A1
20180063743 Tumuluru et al. Mar 2018 A1
20180069924 Tumuluru et al. Mar 2018 A1
20180074909 Bishop et al. Mar 2018 A1
20180077081 Lauer et al. Mar 2018 A1
20180077202 Xu Mar 2018 A1
20180084081 Kuchibhotla et al. Mar 2018 A1
20180091370 Arai Mar 2018 A1
20180097725 Wood et al. Apr 2018 A1
20180114569 Strachan et al. Apr 2018 A1
20180123910 Fitzgibbon May 2018 A1
20180123946 Ramachandran et al. May 2018 A1
20180131608 Jiang et al. May 2018 A1
20180131615 Zhang May 2018 A1
20180131720 Hobson et al. May 2018 A1
20180145899 Rao May 2018 A1
20180159796 Wang et al. Jun 2018 A1
20180159856 Gujarathi Jun 2018 A1
20180167378 Kostyukov et al. Jun 2018 A1
20180176073 Dubey et al. Jun 2018 A1
20180176082 Katz et al. Jun 2018 A1
20180176130 Banerjee et al. Jun 2018 A1
20180176252 Nimmagadda et al. Jun 2018 A1
20180181423 Gunda et al. Jun 2018 A1
20180205746 Boutnaru et al. Jul 2018 A1
20180213472 Ishii et al. Jul 2018 A1
20180219765 Michael et al. Aug 2018 A1
20180219766 Michael et al. Aug 2018 A1
20180234300 Mayya et al. Aug 2018 A1
20180248790 Tan et al. Aug 2018 A1
20180260125 Botes et al. Sep 2018 A1
20180261085 Liu et al. Sep 2018 A1
20180262468 Kumar et al. Sep 2018 A1
20180270104 Zheng et al. Sep 2018 A1
20180278541 Wu et al. Sep 2018 A1
20180287907 Kulshreshtha et al. Oct 2018 A1
20180295101 Gehrmann Oct 2018 A1
20180295529 Jen et al. Oct 2018 A1
20180302286 Mayya et al. Oct 2018 A1
20180302321 Manthiramoorthy et al. Oct 2018 A1
20180307851 Lewis Oct 2018 A1
20180316606 Sung et al. Nov 2018 A1
20180351855 Sood et al. Dec 2018 A1
20180351862 Jeganathan et al. Dec 2018 A1
20180351863 Vairavakkalai et al. Dec 2018 A1
20180351882 Jeganathan et al. Dec 2018 A1
20180359323 Madden Dec 2018 A1
20180367445 Bajaj Dec 2018 A1
20180373558 Chang et al. Dec 2018 A1
20180375744 Mayya et al. Dec 2018 A1
20180375824 Mayya et al. Dec 2018 A1
20180375967 Pithawala et al. Dec 2018 A1
20190013883 Vargas et al. Jan 2019 A1
20190014038 Ritchie Jan 2019 A1
20190020588 Twitchell, Jr. Jan 2019 A1
20190020627 Yuan Jan 2019 A1
20190021085 Mochizuki et al. Jan 2019 A1
20190028378 Houjyo et al. Jan 2019 A1
20190028552 Johnson et al. Jan 2019 A1
20190036808 Shenoy et al. Jan 2019 A1
20190036810 Michael et al. Jan 2019 A1
20190036813 Shenoy et al. Jan 2019 A1
20190046056 Khachaturian et al. Feb 2019 A1
20190058657 Chunduri et al. Feb 2019 A1
20190058709 Kempf et al. Feb 2019 A1
20190068470 Mirsky Feb 2019 A1
20190068493 Ram et al. Feb 2019 A1
20190068500 Hira Feb 2019 A1
20190075083 Mayya et al. Mar 2019 A1
20190081894 Yousaf et al. Mar 2019 A1
20190103990 Cidon et al. Apr 2019 A1
20190103991 Cidon et al. Apr 2019 A1
20190103992 Cidon et al. Apr 2019 A1
20190103993 Cidon et al. Apr 2019 A1
20190104035 Cidon et al. Apr 2019 A1
20190104049 Cidon et al. Apr 2019 A1
20190104050 Cidon et al. Apr 2019 A1
20190104051 Cidon et al. Apr 2019 A1
20190104052 Cidon et al. Apr 2019 A1
20190104053 Cidon et al. Apr 2019 A1
20190104063 Cidon et al. Apr 2019 A1
20190104064 Cidon et al. Apr 2019 A1
20190104109 Cidon et al. Apr 2019 A1
20190104111 Cidon et al. Apr 2019 A1
20190104413 Cidon et al. Apr 2019 A1
20190109769 Jain et al. Apr 2019 A1
20190132221 Boutros et al. May 2019 A1
20190132234 Dong et al. May 2019 A1
20190132322 Song et al. May 2019 A1
20190140889 Mayya et al. May 2019 A1
20190140890 Mayya et al. May 2019 A1
20190149525 Gunda et al. May 2019 A1
20190158371 Dillon et al. May 2019 A1
20190158605 Markuze et al. May 2019 A1
20190199539 Deng et al. Jun 2019 A1
20190220703 Prakash et al. Jul 2019 A1
20190222499 Chen et al. Jul 2019 A1
20190238364 Boutros et al. Aug 2019 A1
20190238446 Barzik et al. Aug 2019 A1
20190238449 Michael et al. Aug 2019 A1
20190238450 Michael et al. Aug 2019 A1
20190238483 Marichetty et al. Aug 2019 A1
20190238497 Tourrilhes et al. Aug 2019 A1
20190268421 Markuze et al. Aug 2019 A1
20190268973 Bull et al. Aug 2019 A1
20190278631 Bernat et al. Sep 2019 A1
20190280962 Michael et al. Sep 2019 A1
20190280963 Michael et al. Sep 2019 A1
20190280964 Michael et al. Sep 2019 A1
20190288875 Shen et al. Sep 2019 A1
20190306197 Degioanni Oct 2019 A1
20190306282 Masputra et al. Oct 2019 A1
20190313278 Liu Oct 2019 A1
20190313907 Khachaturian et al. Oct 2019 A1
20190319847 Nahar et al. Oct 2019 A1
20190319881 Maskara et al. Oct 2019 A1
20190327109 Guichard et al. Oct 2019 A1
20190334786 Dutta et al. Oct 2019 A1
20190334813 Raj et al. Oct 2019 A1
20190334820 Zhao Oct 2019 A1
20190342201 Singh Nov 2019 A1
20190342219 Liu et al. Nov 2019 A1
20190356736 Narayanaswamy et al. Nov 2019 A1
20190364099 Thakkar et al. Nov 2019 A1
20190364456 Yu Nov 2019 A1
20190372888 Michael et al. Dec 2019 A1
20190372889 Michael et al. Dec 2019 A1
20190372890 Michael et al. Dec 2019 A1
20190394081 Tahhan et al. Dec 2019 A1
20200014609 Hockett et al. Jan 2020 A1
20200014615 Michael et al. Jan 2020 A1
20200014616 Michael et al. Jan 2020 A1
20200014661 Mayya et al. Jan 2020 A1
20200014663 Chen et al. Jan 2020 A1
20200021514 Michael et al. Jan 2020 A1
20200021515 Michael et al. Jan 2020 A1
20200036624 Michael et al. Jan 2020 A1
20200044943 Bor-Yaliniz et al. Feb 2020 A1
20200044969 Hao et al. Feb 2020 A1
20200059420 Abraham Feb 2020 A1
20200059457 Raza et al. Feb 2020 A1
20200059459 Abraham et al. Feb 2020 A1
20200067831 Spraggins et al. Feb 2020 A1
20200092207 Sipra et al. Mar 2020 A1
20200097327 Beyer et al. Mar 2020 A1
20200099625 Yigit et al. Mar 2020 A1
20200099659 Cometto et al. Mar 2020 A1
20200106696 Michael et al. Apr 2020 A1
20200106706 Mayya et al. Apr 2020 A1
20200119952 Mayya et al. Apr 2020 A1
20200127905 Mayya et al. Apr 2020 A1
20200127911 Gilson et al. Apr 2020 A1
20200153701 Mohan et al. May 2020 A1
20200153736 Liebherr et al. May 2020 A1
20200159661 Keymolen et al. May 2020 A1
20200162407 Tillotson May 2020 A1
20200169473 Rimar et al. May 2020 A1
20200177503 Hooda et al. Jun 2020 A1
20200177550 Valluri et al. Jun 2020 A1
20200177629 Hooda et al. Jun 2020 A1
20200186471 Shen et al. Jun 2020 A1
20200195557 Duan et al. Jun 2020 A1
20200204460 Schneider et al. Jun 2020 A1
20200213212 Dillon et al. Jul 2020 A1
20200213224 Cheng et al. Jul 2020 A1
20200218558 Sreenath et al. Jul 2020 A1
20200235990 Janakiraman et al. Jul 2020 A1
20200235999 Mayya et al. Jul 2020 A1
20200236046 Jain et al. Jul 2020 A1
20200241927 Yang et al. Jul 2020 A1
20200244721 S et al. Jul 2020 A1
20200252234 Ramamoorthi et al. Aug 2020 A1
20200259700 Bhalla et al. Aug 2020 A1
20200267184 Vera-Schockner Aug 2020 A1
20200267203 Jindal et al. Aug 2020 A1
20200280587 Janakiraman et al. Sep 2020 A1
20200287819 Theogaraj et al. Sep 2020 A1
20200287976 Theogaraj et al. Sep 2020 A1
20200296011 Jain et al. Sep 2020 A1
20200296026 Michael et al. Sep 2020 A1
20200301764 Thoresen et al. Sep 2020 A1
20200314006 Mackie et al. Oct 2020 A1
20200314614 Moustafa et al. Oct 2020 A1
20200322230 Natal et al. Oct 2020 A1
20200322287 Connor et al. Oct 2020 A1
20200336336 Sethi et al. Oct 2020 A1
20200344089 Motwani et al. Oct 2020 A1
20200344143 Faseela et al. Oct 2020 A1
20200344163 Gupta et al. Oct 2020 A1
20200351188 Arora et al. Nov 2020 A1
20200358878 Bansal et al. Nov 2020 A1
20200366530 Mukundan et al. Nov 2020 A1
20200366562 Mayya et al. Nov 2020 A1
20200382345 Zhao et al. Dec 2020 A1
20200382387 Pasupathy et al. Dec 2020 A1
20200403821 Dev et al. Dec 2020 A1
20200412483 Tan et al. Dec 2020 A1
20200412576 Kondapavuluru et al. Dec 2020 A1
20200413283 Shen et al. Dec 2020 A1
20210006482 Hwang et al. Jan 2021 A1
20210006490 Michael et al. Jan 2021 A1
20210021538 Meck et al. Jan 2021 A1
20210029019 Kottapalli Jan 2021 A1
20210029088 Mayya et al. Jan 2021 A1
20210036888 Makkalla et al. Feb 2021 A1
20210036987 Mishra et al. Feb 2021 A1
20210037159 Shimokawa Feb 2021 A1
20210049191 Masson et al. Feb 2021 A1
20210067372 Cidon et al. Mar 2021 A1
20210067373 Cidon et al. Mar 2021 A1
20210067374 Cidon et al. Mar 2021 A1
20210067375 Cidon et al. Mar 2021 A1
20210067407 Cidon et al. Mar 2021 A1
20210067427 Cidon et al. Mar 2021 A1
20210067442 Sundararajan et al. Mar 2021 A1
20210067461 Cidon et al. Mar 2021 A1
20210067464 Cidon et al. Mar 2021 A1
20210067467 Cidon et al. Mar 2021 A1
20210067468 Cidon et al. Mar 2021 A1
20210073001 Rogers et al. Mar 2021 A1
20210092062 Dhanabalan et al. Mar 2021 A1
20210099360 Parsons et al. Apr 2021 A1
20210105199 H et al. Apr 2021 A1
20210111998 Saavedra Apr 2021 A1
20210112034 Sundararajan et al. Apr 2021 A1
20210126830 R et al. Apr 2021 A1
20210126853 Ramaswamy et al. Apr 2021 A1
20210126854 Guo et al. Apr 2021 A1
20210126860 Ramaswamy et al. Apr 2021 A1
20210144091 H et al. May 2021 A1
20210160169 Shen et al. May 2021 A1
20210160813 Gupta et al. May 2021 A1
20210176255 Hill et al. Jun 2021 A1
20210184952 Mayya et al. Jun 2021 A1
20210184966 Ramaswamy et al. Jun 2021 A1
20210184983 Ramaswamy et al. Jun 2021 A1
20210194814 Roux et al. Jun 2021 A1
20210226880 Ramamoorthy et al. Jul 2021 A1
20210234728 Cidon et al. Jul 2021 A1
20210234775 Devadoss et al. Jul 2021 A1
20210234786 Devadoss et al. Jul 2021 A1
20210234804 Devadoss et al. Jul 2021 A1
20210234805 Devadoss et al. Jul 2021 A1
20210235312 Devadoss et al. Jul 2021 A1
20210235313 Devadoss et al. Jul 2021 A1
20210266262 Subramanian et al. Aug 2021 A1
20210279069 Salgaonkar et al. Sep 2021 A1
20210314289 Chandrashekhar et al. Oct 2021 A1
20210314385 Pande et al. Oct 2021 A1
20210328835 Mayya et al. Oct 2021 A1
20210336880 Gupta et al. Oct 2021 A1
20210377109 Shrivastava et al. Dec 2021 A1
20210377156 Michael et al. Dec 2021 A1
20210392060 Silva et al. Dec 2021 A1
20210392070 Tootaghaj et al. Dec 2021 A1
20210392171 Srinivas et al. Dec 2021 A1
20210399920 Sundararajan et al. Dec 2021 A1
20210399978 Michael et al. Dec 2021 A9
20210400113 Markuze et al. Dec 2021 A1
20210400512 Agarwal et al. Dec 2021 A1
20210409277 Jeuk et al. Dec 2021 A1
20220006726 Michael et al. Jan 2022 A1
20220006751 Ramaswamy et al. Jan 2022 A1
20220006756 Ramaswamy et al. Jan 2022 A1
20220029902 Shemer et al. Jan 2022 A1
20220035673 Markuze et al. Feb 2022 A1
20220038370 Vasseur et al. Feb 2022 A1
20220038371 Raiciu Feb 2022 A1
20220038557 Markuze et al. Feb 2022 A1
20220045927 Liu et al. Feb 2022 A1
20220052928 Sundararajan et al. Feb 2022 A1
20220061059 Dunsmore et al. Feb 2022 A1
20220086035 Devaraj et al. Mar 2022 A1
20220094644 Cidon et al. Mar 2022 A1
20220123961 Mukundan et al. Apr 2022 A1
20220131740 Mayya et al. Apr 2022 A1
20220131807 Srinivas et al. Apr 2022 A1
20220131898 Hooda et al. Apr 2022 A1
20220141184 Oswal et al. May 2022 A1
20220158923 Ramaswamy et al. May 2022 A1
20220158924 Ramaswamy et al. May 2022 A1
20220158926 Wennerström et al. May 2022 A1
20220166713 Markuze et al. May 2022 A1
20220191719 Roy Jun 2022 A1
20220198229 López et al. Jun 2022 A1
20220210035 Hendrickson et al. Jun 2022 A1
20220210041 Gandhi et al. Jun 2022 A1
20220210042 Gandhi et al. Jun 2022 A1
20220210122 Levin et al. Jun 2022 A1
20220217015 Vuggrala et al. Jul 2022 A1
20220231949 Ramaswamy et al. Jul 2022 A1
20220231950 Ramaswamy et al. Jul 2022 A1
20220232411 Vijayakumar et al. Jul 2022 A1
20220239596 Kumar et al. Jul 2022 A1
20220294701 Mayya et al. Sep 2022 A1
20220335027 Seshadri et al. Oct 2022 A1
20220337553 Mayya et al. Oct 2022 A1
20220353152 Ramaswamy Nov 2022 A1
20220353171 Ramaswamy et al. Nov 2022 A1
20220353175 Ramaswamy et al. Nov 2022 A1
20220353182 Ramaswamy et al. Nov 2022 A1
20220353190 Ramaswamy et al. Nov 2022 A1
20220360500 Ramaswamy et al. Nov 2022 A1
20220407773 Kempanna et al. Dec 2022 A1
20220407774 Kempanna et al. Dec 2022 A1
20220407790 Kempanna et al. Dec 2022 A1
20220407820 Kempanna et al. Dec 2022 A1
20220407915 Kempanna et al. Dec 2022 A1
20230006929 Mayya et al. Jan 2023 A1
20230025586 Rolando et al. Jan 2023 A1
20230026330 Rolando et al. Jan 2023 A1
20230026865 Rolando et al. Jan 2023 A1
20230028872 Ramaswamy Jan 2023 A1
20230039869 Ramaswamy et al. Feb 2023 A1
20230041916 Zhang et al. Feb 2023 A1
20230054961 Ramaswamy et al. Feb 2023 A1
20230105680 Simlai et al. Apr 2023 A1
20230121871 Mayya et al. Apr 2023 A1
20230164158 Fellows et al. May 2023 A1
20230179445 Cidon et al. Jun 2023 A1
20230179502 Ramaswamy et al. Jun 2023 A1
20230179521 Markuze et al. Jun 2023 A1
20230179543 Cidon et al. Jun 2023 A1
20230216768 Zohar et al. Jul 2023 A1
20230216804 Zohar et al. Jul 2023 A1
20230221874 Markuze et al. Jul 2023 A1
20230224356 Markuze et al. Jul 2023 A1
20230224759 Ramaswamy Jul 2023 A1
20230231845 Manoharan et al. Jul 2023 A1
20230239234 Zohar et al. Jul 2023 A1
20230261974 Ramaswamy et al. Aug 2023 A1
20230308421 Mayya et al. Sep 2023 A1
Foreign Referenced Citations (56)
Number Date Country
1483270 Mar 2004 CN
1926809 Mar 2007 CN
102577270 Jul 2012 CN
102811165 Dec 2012 CN
104205757 Dec 2014 CN
104956329 Sep 2015 CN
106230650 Dec 2016 CN
106656847 May 2017 CN
106998284 Aug 2017 CN
110447209 Nov 2019 CN
111198764 May 2020 CN
1031224 Mar 2005 EP
1912381 Apr 2008 EP
2538637 Dec 2012 EP
2763362 Aug 2014 EP
3041178 Jul 2016 EP
3297211 Mar 2018 EP
3509256 Jul 2019 EP
3346650 Nov 2019 EP
3767897 Jan 2021 EP
2002368792 Dec 2002 JP
2010233126 Oct 2010 JP
2014200010 Oct 2014 JP
2017059991 Mar 2017 JP
2017524290 Aug 2017 JP
20170058201 May 2017 KR
2574350 Feb 2016 RU
2000078004 Dec 2000 WO
03073701 Sep 2003 WO
2005071861 Aug 2005 WO
2007016834 Feb 2007 WO
2012167184 Dec 2012 WO
2015092565 Jun 2015 WO
2016061546 Apr 2016 WO
2016123314 Aug 2016 WO
2017083975 May 2017 WO
2019070611 Apr 2019 WO
2019094522 May 2019 WO
2020012491 Jan 2020 WO
2020018704 Jan 2020 WO
WO-2020001192 Jan 2020 WO
2020091777 May 2020 WO
2020101922 May 2020 WO
2020112345 Jun 2020 WO
2021040934 Mar 2021 WO
2021118717 Jun 2021 WO
2021150465 Jul 2021 WO
2021211906 Oct 2021 WO
2022005607 Jan 2022 WO
2022082680 Apr 2022 WO
2022154850 Jul 2022 WO
2022159156 Jul 2022 WO
2022231668 Nov 2022 WO
2022235303 Nov 2022 WO
2022265681 Dec 2022 WO
2023009159 Feb 2023 WO
Non-Patent Literature Citations (56)
Entry
Funabiki, Nobuo, et al., “A Frame Aggregation Extension of Routing Algorithm for Wireless Mesh Networks,” 2014 Second International Symposium on Computing and Networking, Dec. 10-12, 2014, 5 pages, IEEE, Shizuoka, Japan.
Non-Published Commonly Owned U.S. Appl. No. 18/197,090, filed May 14, 2023, 36 pages, Nicira, Inc.
Non-Published Commonly Owned U.S. Appl. No. 18/211,568, filed Jun. 19, 2023, 37 pages, VMware, Inc.
Author Unknown, “VeloCloud Administration Guide: VMware SD-WAN by VeloCloud 3.3,” Month Unknown 2019, 366 pages, VMware, Inc., Palo Alto, CA, USA.
Non-Published Commonly Owned U.S. Appl. No. 18/137,584, filed Apr. 21, 2023, 57 pages, VMware, Inc.
Non-Published Commonly Owned U.S. Appl. No. 18/222,864, filed Jul. 17, 2023, 350 pages, VMware, Inc.
Non-Published Commonly Owned U.S. Appl. No. 18/222,868, filed Jul. 17, 2023, 22 pages, VMware, Inc.
Non-Published Commonly Owned U.S. Appl. No. 18/224,466, filed Jul. 20, 2023, 56 pages, VMware, Inc.
Non-Published Commonly Owned U.S. Appl. No. 18/235,879, filed Aug. 20, 2023, 173 pages, VMware, Inc.
Alsaeedi, Mohammed, et al., “Toward Adaptive and Scalable OpenFlow—SDN Flow Control: a Survey,” IEEE Access, Aug. 1, 2019, 34 pages, vol. 7, IEEE, retrieved from https://ieeexplore.ieee.org/document/8784036.
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.
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.
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.
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.
Guo, Xiangyi, et al., U.S. Appl. No. 62/925,193, filed Oct. 23, 2019, 26 pages.
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.
Jivorasetkul, Supalerk, et al., “End-to-End Header Compression over Software-Defined Networks: a Low Latency Network Architecture,” 2012 Fourth International Conference on Intelligent Networking and Collaborative Systems, Sep. 19-21, 2012, 2 pages, IEEE, Bucharest, Romania.
Lasserre, Marc, et al., “Framework for Data Center (DC) Network Virtualization,” RFC 7365, Oct. 2014, 26 pages, IETF.
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.
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.
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.
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.
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.
Mishra, Mayank, et al., “Managing Network Reservation for Tenants in Oversubscribed Clouds,” 2013 IEEE 21st International Symposium on Modelling, Analysis and Simulation of Computer and Telecommunication Systems, Aug. 14-16, 2013, 10 pages, IEEE, San Francisco, CA, USA.
Mudigonda, Jayaram, et al., “NetLord: a Scalable Multi-Tenant Network Architecture for Virtualized Datacenters,” Proceedings of the ACM SIGCOMM 2011 Conference, Aug. 15-19, 2011, 12 pages, ACM, Toronto, Canada.
Non-Published Commonly Owned U.S. Appl. No. 17/574,225, filed Jan. 12, 2022, 56 pages, VMware, Inc.
Non-Published Commonly Owned U.S. Appl. No. 17/574,236, filed Jan. 12, 2022, 54 pages, VMware, Inc.
Non-Published Commonly Owned U.S. Appl. No. 17/695,264, filed Mar. 15, 2022, 28 pages, VMware, Inc.
Non-Published Commonly Owned U.S. Appl. No. 17/833,555, filed Jun. 6, 2022, 34 pages, VMware, Inc.
Non-Published Commonly Owned U.S. Appl. No. 17/833,566, filed Jun. 6, 2022, 35 pages, VMware, Inc.
Non-Published Commonly Owned U.S. Appl. No. 17/966,814, filed Oct. 15, 2022, 176 pages, VMware, Inc.
Non-Published Commonly Owned U.S. Appl. No. 17/966,820, filed Oct. 15, 2022, 26 pages, VMware, Inc.
Non-Published Commonly Owned U.S. Appl. No. 17/976,717, filed Oct. 28, 2022, 37 pages, VMware, Inc.
Non-Published Commonly Owned U.S. Appl. No. 18/083,536, filed Dec. 18, 2022, 27 pages, VMware, Inc.
Non-Published Commonly Owned U.S. Appl. No. 18/088,555, filed Dec. 24, 2022, 35 pages, VMware, Inc.
Non-Published Commonly Owned U.S. Appl. No. 18/088,556, filed Dec. 24, 2022, 27 pages, VMware, Inc.
Non-Published Commonly Owned U.S. Appl. No. 18/102,685, filed Jan. 28, 2023, 124 pages, VMware, Inc.
Non-Published Commonly Owned U.S. Appl. No. 18/102,687, filed Jan. 28, 2023, 172 pages, VMware, Inc.
Non-Published Commonly Owned U.S. Appl. No. 18/102,688, filed Jan. 28, 2023, 49 pages, VMware, Inc.
Non-Published Commonly Owned U.S. Appl. No. 18/102,689, filed Jan. 28, 2023, 46 pages, VMware, Inc.
Non-Published Commonly Owned U.S. Appl. No. 15/803,964 (MODE.P004), filed Nov. 6, 2017, 15 pages, The Mode Group.
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.
Ray, Saikat, et al., “Always Acyclic Distributed Path Computation,” University of Pennsylvania Department of Electrical and Systems Engineering Technical Report, May 2008, 16 pages, University of Pennsylvania ScholarlyCommons.
Sarhan, Soliman Abd Elmonsef, et al., “Data Inspection in SDN Network,” 2018 13th International Conference on Computer Engineering and Systems (ICCES), Dec. 18-19, 2018, 6 pages, IEEE, Cairo, Egypt.
Taleb, Tarik, “D4.1 Mobile Network Cloud Component Design,” Mobile Cloud Networking, Nov. 8, 2013, 210 pages, MobileCloud Networking Consortium, retrieved from http://www.mobile-cloud-networking.eu/site/index.php?process=download&id=127&code=89d30565cd2ce087d3f8e95f9ad683066510a61f.
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.
Valtulina, Luca, “Seamless Distributed Mobility Management (DMM) Solution in Cloud Based LTE Systems,” Master Thesis, Nov. 2013, 168 pages, University of Twente, retrieved from http://essay.utwente.nl/64411/1/Luca_Valtulina_MSc_Report_final.pdf.
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.
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.
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.
Zakurdaev, Gieorgi, et al., “Dynamic On-Demand Virtual Extensible LAN Tunnels via Software-Defined Wide Area Networks,” 2022 IEEE 12th Annual Computing and Communication Workshop and Conference, Jan. 26-29, 2022, 6 pages, IEEE, Las Vegas, NV, USA.
Related Publications (1)
Number Date Country
20230216801 A1 Jul 2023 US
Provisional Applications (1)
Number Date Country
63296463 Jan 2022 US