Accelerated network packet processing

Information

  • Patent Grant
  • 11677851
  • Patent Number
    11,677,851
  • Date Filed
    Friday, September 24, 2021
    3 years ago
  • Date Issued
    Tuesday, June 13, 2023
    a year ago
Abstract
Devices and techniques for accelerated packet processing are described herein. The device can match an action to a portion of a network data packet and accelerate the packet-processing pipeline for the network data packet through the machine by processing the action.
Description
TECHNICAL FIELD

Embodiments described generally herein relate to processing of data packets sent or received through a network. Some embodiments relate to hardware acceleration of data packet processing.


BACKGROUND

Hardware switches when combined with specialized hardware functions provide network capabilities including packet switching, security, deep packet inspection, and other capabilities. Recently, there has been a trend in providing virtual switches and virtual functions executing on high-volume computer architectures. The industry has ongoing efforts focused on improving the coordination between the virtual switches for purposes of optimally taking advantage of the throughput benefits, provided by hardware switches along with the flexibility and power of virtual switches.





BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings, which are not necessarily drawn to scale, like numerals may describe similar components in different views. Like numerals having different letter suffixes may represent different instances of similar components. The drawings illustrate generally, by way of example, but not by way of limitation, various embodiments discussed in the present document.



FIG. 1 illustrates components of a single system that deploys multiple switching platforms for implementing accelerated network packet processing in accordance with some embodiments.



FIG. 2 illustrates components of a system that deploys a virtualization environment for implementing accelerated network packet processing in accordance with some embodiments.



FIG. 3 illustrates a control device to accelerate network processing in accordance with some embodiments.



FIG. 4 illustrates a method for accelerating network packet processing in accordance with some embodiments.



FIG. 5 illustrates a system for accelerating network packet processing in accordance with some embodiments.





DETAILED DESCRIPTION

Some network packet processing solutions have focused on hardware the use top-of-rack (ToR) switches and special function hardware to provide network functions including packet switching, security, deep packet inspection, and other functions. However, customers may experience reduced functionality caused by hardware limitations such as limited memory, limited Ternary Content-Addressable Memory (TCAM), reduced total number of supported data flows, etc. Furthermore, hardware switches may be overly rigid with respect to packet parsing, and hardware switches can exhibit a general lack of platform flexibility and configurability.


Therefore, the industry trend has been to provide software-defined networking (SDN) for decoupling network functions from underlying hardware, which can help increase agility and lower costs. Similarly, network function virtualization (NFV) can replace fixed-function hardware with implementations fully deployed in software that runs more cost-effectively on general purpose, standards-based servers, high-volume servers, and the like. However, such software-defined systems may not take advantage of some desirable characteristics of hardware switches.


These software-based solutions include a variety of software-based abstractions to the underlying physical architectures. For example, a virtual switch permits one or more Virtual Machines (VMs) to communicate with one another. A Virtual Network Function (VNF) may include one or more VMs (running different Operating Systems (OSs)) executing on one or more high volume hardware server architectures, hardware switches, hardware storage, and/or cloud infrastructures. VNFs process to provide specialized network processing in place of customized network appliances.


A variety of Application Programming Interfaces (APIs) and software platforms exist in the industry for permitting network automation utilizing virtual switches. A significant benefit of these approaches is the ability to define and customize packet processing rules and corresponding actions at the user-level of the Operating System (OS). One problem with these approaches is the underutilization of the underlying physical hardware switch, because a significant amount of rule matching and action identification for packet processing takes place within the kernel space of the OS and not on the underlying hardware switch, which has substantially better processing throughput.


One approach, associated with better utilization of the underlying switch architecture, is Single Root Input/Output (I/O) Virtualization (SR-IOV). With SR-IOV, an interface is provided that allows a device adapter to separate hardware resources among packet processing functions. However, this is a binary approach where the feature is: on or off. Moreover, SR-IOV activation alters the architecture's configuration and management. Consequently, SR-IOV is a packet processing “offload” solution, rather than a packet acceleration solution.


Embodiments provide a way to accelerate existing virtual switch solutions to better utilize the underlying physical switch hardware without changing or modifying how the existing virtual switch solutions interact with upper layers of device management and configuration.


Packet processing coordinates and manages, in a fine-grained fashion, multiple data plane components to utilize desirable features of both hardware switching and SDN/NFV usages. Control plane components include mechanisms to determine where traffic (e.g., data packets or flows) should be directed, while data plane components include mechanisms that forward traffic to those destinations. Embodiments provide control plane methods, systems, and devices to accelerate packet processing for multiple data plane components. Data plane components can include, by way of non-limiting example, Data Plane Development Kit (DPDK) components, field programmable gate array (FPGA) components, and Red Rock Canyon (RRC)/FM10K switch components available from Intel of Santa Clara, Calif., among other components. Methods in accordance with various embodiments can coordinate utilization of these and other components in a dynamic and flexible fashion based on user-defined and user-configured actions to reduce or minimize energy consumption or to enhance speed and performance. In embodiments, a control plane can offload a simple fast packet-processing pipeline from software-based switches or virtual switches to switch hardware, while providing for more-complicated processing on the CPU-based software data plane.



FIG. 1 illustrates components of a single system 100 that deploys multiple switching platforms for implementing methods in accordance with some embodiments. Illustration of the embodiments present just those components necessary for appreciating the depicted embodiments, such that other components are foreseeable without departing from the teachings herein.


The system 100 implements techniques having enhanced match-action acceleration in existing software-based packet processing pipelines (series of packet processing stages) for optimized increased bandwidth, lower latency and jitter, and lower Central Processing Unit (CPU) consumption. Match-action processing refers to a mechanism by which the system 100 enables packet processing acceleration. Match-action resources found in the system 100 execute this acceleration. Existing software-based control planes 101 and 102 (such as OpenStack, OpenDaylight, etc.) do not need modification in order to realize the acceleration presented herein. In addition, existing virtualization constructs (providing virtualized environment management), such as Virtio 113 and 133 do not need modified to use the architecture underlying the system 100, which include the optimizations for packet acceleration.


There are no additional interfaces or control planes, which a user is, required to learn in order to realize the optimizations presented herein with respect to accelerated packet processing/communication between devices (such as VMs managed by VNFs 110, 120, and 130).


Specifically, a Network Interface Controller (NIC) provides one or more novel physical functions (PF 141) and one or more virtual functions (VFs 142 and 143). These functions 141-143 respond to one or more novel match-action (e.g., P4) tables and cooperate with existing control planes 101 and 102 and their existing API commands for executing enhanced network packet processing between VNFs (110, 120, and 130) by offloading software-based match-action network packet processing to the underlying switch architecture of the NIC 140. The processing occurs below the operating system (OS) kernel stack 115 on the NIC 140 within TCAM, the PFs 141, and/or the VFs 142 and 143.


The functions 141-143 provide: 1) virtual switch (vSwitch) acceleration 118, 2) vSwitch offloading 126, 3) VNF acceleration 124, and 4) vitrio (virtualization (VM)) acceleration 135.


It is to be noted that although the system 100 depicts functions 141-143 supporting the four types of acceleration (118, 124, 126, and 135); this does not have to be the case in every instance. That is, in other embodiments, the NIC can implement one and/or configured for supporting, all, or various combinations of the four types of acceleration (118, 124, 126, and 135).


The functions (141, 142, and 143) and the supported four types of network packet acceleration (118, 124, 126, and 135) through match-action processing is as follows (with reference to the system 100 of the FIG. 1).


It is noted that the VNF 110 is programmed for processing (on the underlying hardware of the system 100) a variety of software, such as and by way of example, a network application 111 (performing one or more specialized network packet processing operations), a Data Plan Development Kit 122 (DPDK 122) API, and a virtualization service (virtio 113). The VNF 120 includes a network application 121, a DPDK API option 122, and a NIC VF Driver 123. The VNF 130 includes a network application 131, a DPDK API option 132, and a virtualization service (virtio 133).


The VNFs 110, 120, and 130 situated above the vSwitch 114. The vSwitch 114 situated above the kernel stack 115. Access by the vSwitch for matching and accessing the PF 141 over link 117. Access for accessing VF 142 by the NIC VF driver 123 of VNF 120 over link 125 (for direct TLV lookups through VF 142). Access for the VF 143 by the virtio instance 133 of VNF 130 over links 134 and 137 through 136 for virtio TLV driven lookups 134 and match TLV lookups 137 directly through the VF 143.


vSwitch Acceleration

By employing metadata generated in response to a match action table (e.g., P4 file(s)), acceleration of the packet-processing pipeline for the vSwitch 114 occurs.


A P4 file describes capabilities of the match-action pipeline. In an embodiment, the structure of the match-action table is a P4 file (defined by p4.org open source programming language and formats) is:


[Parser]-[Ingress Quality of Service (QoS)]-[Filtering]-[Tunnel/Network Address Translation (NAT)]-[Replication]-[Egress QoS]


In an embodiment, the parser is in P4 format as well. This also provides frame formats (packet payload formats) for matching.


Additionally, P4 provides the mechanism for defining the match filed. A non-limiting set of files can appear as follows:
















L1
L2
L3
L4
>L5







Source
Destination
Source
Destination L4
Virtual


Port
Media Access
Internet

Network



Control (DMAC)
Protocol (IP)

Index




(4/6)

(VNI)


Source
Source Media
Destination
Source L4
Network


Virtual
Access Control
IP (4/6)

Service


Port
(SMAC)


Header






(NSH) Path



Ethernet
Protocol
Transmission
NSH





Control
Service





Protocol (TCP)




Outermost Virtual


L4 TCAM



Local Area






Network (VLAN)






Second






Outermost VLAN






Outermost






Multiprotocol






Label Switching






(MPLS)






Second MPLS









The parse tree also recognizes a format for inner header tunnel-encapsulated frames:















Inner Header
Inner L2
Inner L3
Inner L4







VNI
DMAC
Source IP (4/6)
Destination L4



SMAC
Destination IP (4/6)
Source L4



Ethernet
Protocol




Outermost VLAN









P4 also provides a mechanism for specifying the supported actions, as well as which tables support which actions. A non-limiting example set of actions can be as follows:

















Tunnel/NAT



Basic Actions
Modify Actions
Actions
Replication







Count
Set VLAN/VPRI
Virtual Extensible
Mirror




Local Area Network





(VXLAN)



Permit/deny/drop
Push VLAN
Decapsulate
Multicast




VXLAN



Forward to port
Pop VLAN
Encapsulate NSH
Sample


Forward to virtual
Set Differentiated
Decapsulate NSH



port
Service Code Point





(DSCP)




Spread across
route




ports






Set_soft_id










vSwitch Acceleration Using Metadata


The host Ethernet controller (e.g., NIC 140) processes received-received frames first on system 100, and then the host Ethernet controller passes the processed frames up into the various software layers of the system 100 for further additional processing. On the receive path, the Ethernet controller can pre-process the frames and associate with the frame additional metadata. The semantics of this pre-processing are match-action based. The following examples illustrate the processing by which the vSwitch 114 (such as Open vSwitch (OvS) 114) pipeline processing accelerates with embodiments presented herein.


TCAM Pre-Classify


The host controller pushes received packet frames into the TCAM in the pipeline. The vSwitch 114 programs a set of matching rules into the TCAM (using and existing API of the vSwitch 114, such as an API associated with OvS). This causes some amount of metadata being set that include the result of the TCAM lookup. The PF 141, upon matching, appends the result (extra metadata and either in an encapsulating VLAN or a soft_id_value) within the received frame. The software pipeline uses that result in the metadata embedded in the received frame and can avoid having to achieve this TCAM lookup within the software pipeline.


In an embodiment and in the case of OvS (114), an enhanced-patch to OvS 114 detects this added metadata in the packet. If the metadata does not exist in the packet header in the packet header, the OvS 114 pursues its normal processing path. However, the OvS 114 skips 117 its typical software-based TCAM lookup processing within its pipeline when the metadata exists in the packet header (an indication that the PF 141 previously appended the TCAM look results within the NIC 140 before the OvS 114 processes the packet).


Tunnel Decapsulation


The host controller 140 receives a frame, the vSwitch 114 programs rules that map outer VXLAN, VXLAN-Generic-Protocol-Extension (GPE)+NSH, Generic Network Virtualization Encapsulation (Geneve), or Network Virtualization using Generic Routing Extension (NVGRE) headers into the metadata. In an embodiment, The PF 141 processes these rules for: 1) matching the rules on outer header (L2, L3, VNI, and Service Header); 2) decapsulates the matched outer header, and 3) and adds some additional metadata for signaling the removed header. The pipeline processing detects the removed outer header and processes the inner header accordingly.


In the case of OvS 114 (vSwitch 114), the PF 141 uses the decapsulation processing in conjunction with TCAM pre-classify processing for providing metadata on the outer header as well as metadata on the rules matched in the TCAM. When used in conjunction, the TCAM pre-classify rules apply to the inner header.


vSwitch Tap


Tapping an interface can be used for monitoring packet processing by copying some or all of the frames (associated with the interface) and sending the frames to a different location. This unduly taxes the software and hardware resources of the system 100 and is intrusive to the vSwitch 114 processing. Therefore, the teachings presented herein are particularly beneficial in such a scenario. The system 100 achieves this copying using a match rule that maps in the match-action table to a mirror action, which the functions (141-143) process within the NIC 140.


In an embodiment, the vSwitch 114 sends the frames and then processes for multicast replication, tunnel encapsulation, and monitoring in the accelerated pipeline occurs after the frames occurs. In this manner, performance of packet acceleration takes place during transmit of the packets.


vSwitch Offload

vSwitch offload 126 is an extension of vSwitch acceleration 118 when the following conditions are true of a given set of traffic (network packets being processed through the system 100):


1) the accelerated pipeline has a direct connection 125 to the VM (associated with the traffic managed within VNF 120); and


2) the accelerated pipeline has the capability to fully process the traffic with all of the same rules (which the vSwitch 114 data plane processes).


Although identified as an “offload,” the rules within the vSwitch 114 never leave the vSwitch 114; here, the processing pushes and copies the rules into the accelerated pipeline, and pushes statistics for these rules back to the vSwitch 114 so that the rules stay in the software tables. A common configuration where vSwitch offload 126 can occur is when using OvS 114 to implement rules. The rules are applied by a separate control plane software layer; for example, OpenStack and OpenDaylight (e.g., Third Party APIs 101 and 102). The control flow is as follows:


1) A tunnel manager enumerates the virtual switches (multiple instances of vSwitch 114 not shown in the FIG. 1) and creates tunnels between them in a full mesh topology (every vSwitch connects to every other vSwitch). It is noted that in some embodiments, when vSwitches need to converse with one another, creation of the tunnels occur (lazy processing fashion).


2) When two VMs/containers want to connect to one another (from the VNFs 110-130), population of the forwarding rules to the VMs/containers respective vSwitches occurs. Certain systems will populate these rules via virtual L2 learning; other systems will provision the rules directly from a centralized controller.


In addition to tunnel rules (as discussed above), a vSwitch 114 can implement Access Control Lists (ACLs), service function classification, service function forwarding, basic ingress QoS, and connection tracking.


VNF Acceleration

In both vSwitch acceleration 118 and vSwitch offload 126, the vSwitch 114 is executing in the host system 100 and has full control of the underlying device associated with the system 100 for acceleration processing. Here, the connecting VM/container does not have any control over any sort of acceleration “from the network.” The VM/container does not have any control over what happens inside the vSwitch 114. The VNF 120 uses the same semantics as the vSwitch 114 does for: discovering which tables are available, how many entries are available in each table, and what match-action rules for application. However, restriction of the VNF 120 occurs in the following manners:


1) The host system 100 trusts the VNF 120 for enabling the acceleration (otherwise, the host system 100 cannot honor requests from the VNF 120).


2) Application or the VNF's rules occur on traffic sent to or received from that VNF 120. Therefore, the focus in VNF acceleration 124 is on grooming traffic before sending that traffic to the VNF 120. Thus, the VNF 120 cannot add rules to affect traffic going to/from other VFs (such as VF 143), other PFs 141, or the physical uplink ports.


3) The VNF 120 does not have visibility beyond its own interface. Therefore, the VNF 120 cannot add rules affecting traffic going to/from other VNFs (110 and/or 130), PFs 141, VFs 141 and 143, or physical uplink ports.


4) The VNF 120 may not have the same sets of actions available to it. That is, the VNF functions are generally limited to functions related to the VNF 120, such as ACLs (drops, counts, and policing), steering packets to queues, prioritizing packets into queues, and marking packets with metadata for pre-processing.


In an embodiment, the VNF 120 has multiple queues associated with that VNF 120. The VNF 120 is programmed (using for example Data Plan Development Kit 122 (DPDK 122) API) in the following manner:


1) Configure a default Receive Site Scaling (RSS) rule to spread traffic across multiple queues. Here, the VNF 120 can associate a CPU core for each queue to scale the packet processing across multiple flows.


2) Configure a set of FlowDirector® (Intel® packet steering product) rules that work as exceptions to the RSS default-spreading rule, which places specific flow types and mega flows into specific flows or given specific priorities. When RSS cannot efficiently spread with RSS, the configuration of the FlowDirector® aids in handling that traffic. The configuration of the FlowDirector® also aids in handling high priority traffic.


3) Configure a set of filtering rules to drop or police traffic. This can protect the VNF 120 from unwanted traffic, or from receiving traffic at too high a rate.


4) In a manner similar to vSwitch acceleration 118, the VNF 120 can also associate a 16-bit software identifier (ID) on flows, based on match-action rules. This can accelerate certain forwarding paths within the VNF 120 (such as TCAM processing).


Virtio Acceleration

Virtio acceleration 135 is a variant of vSwitch offload 126 still requiring that the hardware of the system 100 be capable of fully forwarding and performing packet processing the frame, but there is no direct connection between the VNF 130/VM and the underlying hardware (134 and 137). Instead, the VNF 130 connects (via software) with a virtio driver 136. In this scenario, there is a software loop (this may be OvS with the DPDK netdev, or some other entity that is copying frames to/from the VF 143 and the virtio queues within the VF 130/VM. A presented scenario implementing OvS with the DPDK netdev is as follows.


Discussion of the virtio acceleration provided with reference to the FIG. 2.


Although embodiments of FIG. 2 are within the context of virtio that other embodiments are deployable utilizing any available virtualization management system.



FIG. 2 illustrates components of a system 200 that deploys a virtualization environment (such as virtio) for implementing accelerated inter-device communication in accordance with some embodiments. Illustration of the embodiments present just those components necessary for appreciating the depicted embodiments, such that other components are foreseeable without departing from the teachings herein.


Again, system 200 illustrates a number of VNFs (210, 220, and 230) each having a network application (211, 221, and 231), DPDK option APIs (212, 222, and 232), and an instance of virtio (virtualization management services —213, 223, and 233). Each virtio instance (214, 223, and 233) connected to a corresponding vHost (241, 242, and 243) having the ability for virtio TLV lookups via 214, 224, and 234.


The vHosts (241, 242, and 245) situated in the OS with a vSwitch 240 and service provided by DPDK 244. The kernel stack 250 situated in the OS under the vSwitch 240 and the DPDK 244. The kernel stack 250 having direct access to the PF 261 and the DPDK 244 and vSwitch 240 having direct access to the VFs 262-265. Match TLVs lookup configurations occur over 266-268. Link 251 providing operations offloaded to the PF 261. The PF 261 and the VFs 262-265 part of the NIC 260.


Underneath OvS, instantiation of N (number of) DPDK netdevs (Linux virtual unction netdevs-functions for obtaining state and capabilities) occurs. Each netdev corresponds to a VF (such as VFs 261 and 262) within the hardware. The arrangement of ports underneath OvS then appears as follows:


1) PFs accelerated ports attached as regular non-DPDK to the OvS kernel data path. Using these interfaces, when a flow requires processing by the kernel (requires TCP, IP tables, etc.).


2) Default Rule VF(s): between 0 and 4 VFs, hardware accessible for sending frames (which are incapable of forwarded a packet processed) to the DPDK user space pipeline for processing. These VFs (such as VFs 142 and 143) have a similar function as the PF 141 ports, except that the packet processing data plane is in the DPDK (with higher performance but without kernel stacks).

    • In an embodiment, if the underlying Red Rock Canyon (RRC) switch components available from Intel® of Santa Clara, Calif., can support 50 G of bandwidth with on VF (142 or 143) with additional assignment of bandwidth allocated by connecting multiple VFs (142 and 143) from different PFs 117 into one virtio queue.


3) Virtio Alias VFs: remaining VFs (142 or 143 or 261-265 of FIG. 2) used as virtio alias ports and are under the control of the DPDK user-space Poll Mode Driver (PMD).


When attachment of a new VNF 130/VM to the OvS occurs, allocation of this VF 130 occurs as a virtio alias VF. When this happens, the OvS with the DPDK netdev enables a set of “Hardware Offload Virtual (HOV)” paths between its virtual Host (vHost) implementation and the virtio alias VF. Installation of the software logic is as follows:


1) If all of the rules pertaining to the packets coming FROM this VNF 130/VM has been put into the hardware, then zero-copy directly from the vHost into the corresponding virtio alias VF.


2) Else if a partial set of rules pertaining to the packets coming FROM this VF 130/VM has been put into the hardware, process this frame through the OvS user-space pipeline, and when finished push the frame into the corresponding virtio alias VF.


3) Else pass the packet through the OvS packet-processing pipeline. The packet can be sent into the network either via the PF netdevs (the frames are directly forwarded out of a port), via the DPDK VF 130 (in which case the transmitting uplink chosen by the hardware that uses the embedded switch beneath it, or pushed into the kernel for processing via Kernel NIC Interface (KNIC).


4) In the other direction, all received packets on the virtio alias VFs are sent directly from the hardware into the software virtio queues (via vHost) on the HOV path (in an embodiment, a third-party application may provide a zero-copy implementation for doing this).


In the hardware, plumbing of the reverse path occurs and the processing looks similar:


1) If application of all of the rules pertaining to the packets coming TO this VF 130/VM occurred, then forwarding the frame to the VF 130 corresponding to this VM. The PMD picks up this frame and pushes the frame to the VM over the HOV path.


2) If application of a partial set of rules pertaining to this packet occurred, or if this packet misses in the hardware tables, forward the frame to the default rule VF for handling. These frames picked up by the PMD and processed in the DPDK user-space pipeline.


3) Optionally, specific flows needing kernel processing trapped to the PF 141 for standard kernel processing.


When programming the hardware, rules that split traffic between virtio alias, default user space, and kernel data paths preserve conversational ordering. That is one rule does not forward a particular 5-tuple (Destination IP, Source IP, L4-Destination, L4-Source, Protocol) via one path and then forward another frame with the same 5-tuple through another path.


The inverse processing used on the software side: the vHost arbitrates between frames coming directly from the virtio alias, DPDK user-space and kernel data paths. As these are already in conversational order, just process a scheduling function. The virtio alias has highest processing priority, followed by the DPDK user-space and then kernel path.


In this scenario, a VNF 130 may request acceleration by sending Tag-Length-Values (TLVs) via virtio. If this VNF 130 has an aliased VF, these TLVs converted into VNF acceleration requests over a VF mailbox. For this reason and in an embodiment, a configuration provides support for multi-queue virtio, since this allows the VNF 130 to enable RSS and FlowDirector® to spread traffic to multiple queues within the VM. For VNFs using multiple cores, this allows the hardware to spread traffic to multiple cores appearing the connection between VNF 130 and the VF 130 is a direct connection.


In a scenario in which the DPDK or kernel data paths completely process the traffic, the processing through system 100 occurs in two manners:


1) In-line: The first data path to process the frame (hardware packets from the network, software for packets from the vHost) can minimally process the frame and send it to the primary data plane (user space, kernel, or hardware). This is a very efficient mechanism for choosing between data paths, as it does not “bounce-back” frames between data paths, but it does necessitate some amount of processing by the first data path, which may be redundant or overhead on top of the primary data path doing the processing.


2) Bounce-Back: In this scenario software “fastpaths” frames into the hardware, which then loopbacks frames back to the DPDK or the kernel if it cannot handle the frame itself. This has a lower amount of software overhead, but utilizes extra Peripheral Component Internet express (PCIe) bandwidth when frames bounced-back from the hardware back into the software.


In an embodiment, the above-mentioned techniques implemented as methods, an apparatus, and a system for computing-device architectures for achieving accelerated inter-device communication or operations (such as inter-VM communication on-chip for a single device architecture). This provides acceleration for one, all, or combinations of: 1) vSwitch acceleration using metadata, 2) offloaded vSwitch for Virtio connected VMs, 3) offloaded vSwitch for NSH service chaining, and/or 4) Multi-Layer Traffic Pre-Classify (VNF acceleration and NSH service chaining.


These and other embodiments of accelerated inter-device communication presented below with reference to the FIGS. 3-5.



FIG. 3 illustrates a control device 300 to accelerate network processing in accordance with some embodiments. The control device 300 includes a memory 301, processor 302 having instructions 303, a switch interface 304, one or more data plane interfaces 305. The control device 300 interacts with switch silicon 310 (through the switch interface 304) and one or more data plane processor(s) 320 (through the data plane interface(s) 305).


In an embodiment, the control device 300 is a NIC.


In an embodiment, the control device 300 is the NIC 140 of the FIG. 1.


In an embodiment, the control device 300 is the NIC 260 of the FIG. 2


In an embodiment, the control device 300 integrated within and/or interfaced to a multi-core processor. In an embodiment, the multicore processor is a server.


In an embodiment, the control device 300 operates within multiple heterogeneous and virtualized processing environments having a variety of VMs, operating systems, etc.


The control device 300 includes processor 302 to perform functionalities as described herein. It will be understood that any or all of the functions performed by processor 302 can be executed with hardware, software, firmware, or any combination thereof, on one or more processing cores, for example Intel® Architecture cores 114 or a core of the control device 300.


In an embodiment, the processor 302 performs the processing described above with respect to the PF 141 and the VFs 142 and 143 of the FIG. 1.


In an embodiment, the processor 302 performs the processing described above with respect to the PF 261 and the VFs 262-265.


In embodiments, the processor 302 can match an action referenced from a table to a portion of data in a network packet. The processor 302.


In an embodiment, the processor 302 is at least partial programmed by an Application Programming Interface (API). In an embodiment, the API provided in formats supported by one or more of: DPDK, OvS, OpenDaylight, and OpenStack.


In an embodiment, the processor 302 is programmed to be responsive to one or more tables or files that identify actions that the processor 302 is responsive to for purposes of performing predefined processing based on a particular identified action.


In an embodiment, a user interface permits a user to access the API for populating the table or file with actions.


In an embodiment, the file or table is the match-action files discussed above with reference to the FIGS. 1-2.


The processor 302 can match an action reference from a file/table to a portion of a packet header for a received network packet. The processor 302 processes the action identified by the action reference in cooperation with the memory 301 to accelerate a packet-processing pipeline for the network packet.


In an embodiment, the memory 301 is Random Access Memory (RAM) on the control device 300.


Processing the action does not have to be a single operation; rather, the action can trigger the processor 302 to perform a series of predefined operations.


In an embodiment, the processor 302 when processing the action performs a Ternary CAM lookup on some of the packet data. This was discussed above in the FIGS. 1-2 and specific with relevance to acceleration using packet pre-classify processing performed at the NIC.


In an embodiment, the processor 302 when processing the actions decapsulates an outer tunnel encapsulated header for the network packet, removes that header, and adds metadata into the header indicating that the header was removed. This processing discussed above with reference to the FIG. 1.


In an embodiment, the processor 302 when processing the actions copies a packet payload (frame) from the network packet and then sends that copied payload to a network location, which is independent of network locations, defined within or identify resources for processing the packet-processing pipeline. This scenario describes network traffic mirror and monitor operations having substantial improvements in processing throughput since resolution, processing, and sending for mirror packets occur on the control device 300 and not at upper layers within the network architecture, such as the OS layer for a specific VM, and the like.


In an embodiment, the processor 302 when processing the action copies the network packet forwarding rules from a first virtual switch to a second virtual switch. This scenario discussed above with reference to vSwitch offload acceleration and the discussion of the FIG. 1.


The processor 302 configured to communicate with the underlying switch silicon 310 through a switch interface 304.



FIG. 4 illustrates a method 400 for accelerating network packet processing in accordance with some embodiments. The method 400 implemented as executable instructions represented by one or more software modules (method 400 herein after referred to as “packet accelerator”) and executed by one or more hardware processor(s) from a non-transitory computer-readable storage medium.


In an embodiment, the executable instructions representing the packet accelerator is stored in a non-transitory computer-readable storage medium that when executed by one or more computing devices performs the method 400 processing.


In an embodiment, the packet accelerator configured for execution as firmware on a NIC. In an embodiment, the packet accelerator is the PF 141 of the FIG. 1. In an embodiment, the packet accelerator is the VF(s) 142 and/or 143 of the FIG. 1. In an embodiment, the packet accelerator is the PF 261 of the FIG. 2. In an embodiment, the packet accelerator is the VF(s) 262-265 of the FIG. 2.


In an embodiment, the packet accelerator configured for execution within one or more virtual switches, one or more physical switches, one or more device interfaces, one or more virtual device interfaces, and/or one or more operating systems.


In an embodiment, the packet accelerator configured for execution within one or more independent and virtualized environments (execution on system 100 and/or system 200 of the FIGS. 1 and 2, respectively).


In an embodiment, the packet accelerator configured for execution as the control device 300 of the FIG. 3.


At 410, the packet accelerator matches a portion of a network data packet to an action in a match-action table. For example, the processing and file structures of match-action files and matching to network packets discussed in the FIG. 1 above.


At 420, the packet accelerator accelerates processing of the network packet through the machine by executing the action as part of a packet-processing pipeline for the network data packet. That is, action processing accelerates packet processing in a number of manners that can include processing for: 1) offloading processing of software-based resources to hardware-based resources; 2) reorganizing processing of the software-based resources; and/or 3) reorganizing how the software-based resources access the hardware-based resources.


According to an embodiment, at 421, the packet accelerator inserts metadata into the network data packet as an indication that a portion of the packet processing has been handled/processed when the action processed. For example, the pre-classify processing and the TCAM processing presented above in the FIGS. 1-2 provides discussion relevant this processing.


In an embodiment, at 422, the packet accelerator copies a packet frame for the network data packet and sends the copied packet frame to a location that is independent of locations associated with the packet-processing pipeline. This is a packet mirroring processing situation discussed above with reference to the FIG. 1.


In an embodiment, at 423, the packet accelerator assigns the network data packet to a queue associated with a specific processing core. This may entail other configuration dependencies and processing presented above with the discussion of the virtio acceleration processing of the FIG. 2.


In an embodiment of 423 and at 424, the packet accelerator filters the network packet responsive to filtering rules.


In an embodiment of 424 and at 425, the packet accelerator sets a resource identifier on the network data packet that identifies a resource for processing against the network data packet when processing the network data packet through the packet-processing pipeline. So, tables, metadata, and/or the packet-processing pipeline control structures providing a mechanism by which different heterogeneous software and/or hardware resources that assist in accelerating the network data packet through the packet-processing pipeline (network/device(s)), and these mechanisms identified as a resource identifier for the network data packet. The packet accelerator configured for identifying the resource identifier and causing the corresponding resource to process for accelerating the pipeline processing of the packet through the network/device(s).


In an embodiment, at 426, the packet accelerator performs a variety of alternative processing depending on whether traffic rules are fully offloaded to hardware or other conditions. Such conditions discussed with reference to the virtio acceleration of the FIG. 2. Thus, the packet accelerator can: 1) zero-copy rules for processing the network data packet into an aliased-virtualized function; 2) process a frame for the network data packet through a user-space of an OS and when finished push the frame into the aliased virtualized function; or 3) pass the network data packet through the user-space pipeline for the OS.


Examples, as described herein, may include, or may operate on, logic or a number of components, modules, or mechanisms. Modules are tangible entities (e.g., hardware) capable of performing specified operations and may be configured or arranged in a certain manner. In an example, circuits may be arranged (e.g., internally or with respect to external entities such as other circuits) in a specified manner as a module. In an example, at least a part of one or more computer systems (e.g., a standalone, client or server computer system) or one or more processors of the control device 300 may be configured by firmware or software (e.g., instructions 303 (FIG. 3), an application portion, or an application) as a module that operates to perform specified operations. In an example, the software may reside on at least one machine-readable medium. In an example, the software, when executed by the underlying hardware of the module (e.g., the control device 300), can include instructions 303 (FIG. 3) to cause the hardware to perform the specified operations.


The term “module” is understood to encompass a tangible entity, be that an entity that is physically constructed, specifically configured (e.g., hardwired), or temporarily (e.g., transitorily) configured (e.g., programmed) to operate in a specified manner or to perform at least part of any operation described herein. Considering examples in which modules are temporarily configured, a module need not be instantiated at any one moment in time. For example, where the modules comprise a general-purpose hardware processor configured using software; the general-purpose hardware processor may be configured as respective different modules at different times. Software may accordingly configure a hardware processor, for example, to constitute a particular module at one instance of time and to constitute a different module at a different instance of time. The terms “application, process, or service,” or variants thereof, is used expansively herein to include routines, program modules, programs, components, and the like, and may be implemented on various system configurations, including single-processor or multiprocessor systems, microprocessor-based electronics, single-core or multi-core systems, combinations thereof, and the like. Thus, the terms “application, process, or service” may be used to refer to an embodiment of software or to hardware arranged to perform at least part of any operation described herein.


While a machine-readable medium may include a single medium, the term “machine-readable medium” may include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers).


The term “machine-readable medium” may include any medium that is capable of storing, encoding, or carrying instructions 303 for execution by a machine (e.g., the control device 300 or any other module) and that cause the machine to perform any one or more of the techniques of the present disclosure, or that is capable of storing, encoding or carrying data structures used by or associated with such instructions. In other words, the processor 302 (FIG. 3) can include instructions and can therefore be termed a machine-readable medium in the context of various embodiments. Other non-limiting machine-readable medium examples may include solid-state memories, and optical and magnetic media. Specific examples of machine-readable media may include: non-volatile memory, such as semiconductor memory devices (e.g., Electrically Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM)) and flash memory devices; magnetic disks, such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks.


The instructions 303 may further be transmitted or received over a communications network using a transmission medium utilizing any one of a number of transfer protocols (e.g., frame relay, internet protocol (IP), TCP, user datagram protocol (UDP), hypertext transfer protocol (HTTP), etc.). Example communication networks may include a local area network (LAN), a wide area network (WAN), a packet data network (e.g., the Internet), mobile telephone networks ((e.g., channel access methods including Code Division Multiple Access (CDMA), Time-division multiple access (TDMA), Frequency-division multiple access (FDMA), and Orthogonal Frequency Division Multiple Access (OFDMA) and cellular networks such as Global System for Mobile Communications (GSM), Universal Mobile Telecommunications System (UMTS), CDMA 2000 1×* standards and Long Term Evolution (LTE)), Plain Old Telephone (POTS) networks, and wireless data networks (e.g., Institute of Electrical and Electronics Engineers (IEEE) 802 family of standards including IEEE 802.11 standards (WiFi), IEEE 802.16 standards (WiMax®) and others), peer-to-peer (P2P) networks, or other protocols now known or later developed.


The term “transmission medium” shall be taken to include any intangible medium that is capable of storing, encoding or carrying instructions for execution by hardware processing circuitry, and includes digital or analog communications signals or other intangible medium to facilitate communication of such software.



FIG. 5 illustrates a system 500 for accelerating network packet processing in accordance with some embodiments. The system 500 shown in greatly simplified format with just particular components illustrated that are necessary for comprehending the system 500.


In an embodiment, the system is a multi-core server 510.


In an embodiment, the multi-core server 510 configured for performing vSwitch acceleration in the manners discussed above with reference to the FIG. 1.


In an embodiment, the multi-core server 510 configured for performing vSwitch offload acceleration in the manners discussed above with reference to the FIG. 1.


In an embodiment, the multi-core server 510 configured for performing VNF acceleration in the manners discussed above with reference to the FIG. 1.


In an embodiment, the multi-core server 510 configured for performing virtio acceleration in the manners discussed above with reference to the FIGS. 1 and/or 2.


In an embodiment, the multi-core server 510 configured for performing selective network packet acceleration for: vSwitch acceleration, vSwitch offload acceleration, VNF acceleration, and/or virtio acceleration in the manners discussed above with reference to the FIGS. 1 and/or 2.


The system 500 includes a means for matching a portion of a network packet to an action and a means for processing the action to accelerate a packet-processing pipeline, and, optionally, a means for configuring the means for matching and the means for processing.


The means for matching a portion of a network packet to an action during network-packet processing.


In an embodiment, the means for matching includes a match-action table 521 or file, such as a P4 file as discussed above with reference to the FIG. 1.


In an embodiment, the means for matching includes a memory, such as memory 301 of the FIG. 3.


In an embodiment, the means for matching includes volatile or non-volatile memory on a NIC, such as NIC 140 or 260 of the FIGS. 1 and 2, respectively.


In an embodiment, the means for matching includes volatile or non-volatile memory accessible on one or more devices representing the system 500.


In an embodiment, the means for matching includes a combination of memory and storage accessible on one or more device representing the system 500.


The means for processing configured to process the action (obtained from the means for matching). The action processed to accelerate a packet-processing pipeline (associated with or assigned to) for the network packet.


In an embodiment, the means for processing is one or more of: one or more device driver(s) (interface(s)) 540, one or more virtual interface(s) 540, one or more virtual switch(es) 550, one or more OS kernel processes (560), and/or a NIC with a physical switch 570.


In an embodiment, the means for processing is various combinations of the component devices and modules illustrated in the FIGS. 1, 2, and/or 3.


In an embodiment, the system 500 includes a means for configuring the means for processing to custom-define (or user-define) the action.


In an embodiment, the means for configuring is the API 520. In an embodiment, the API 520 is a specific configured instance of components illustrated in the FIGS. 1 and 2 using an API provided with OvS, DPDK, OpenStack, OpenDaylight, and/or virtio.


In an embodiment, the means for configuring also provides a means for configuring the means for matching. In an embodiment, the API 520 is the means for configuring and permits tables of files to be established for action in the match-action table/file 521.


Additional Notes & Examples

Example 1 includes subject matter (such as a control device, interplane control device, control plane processor, computer device and or any other electrical apparatus, device or processor) including a memory and processing circuitry. The processing circuitry configured to match an action reference from a table to a portion of data in a network data packet. The processing circuitry further configured to process an action identified by the action reference in cooperation with the memory that accelerates a packet-processing pipeline for the network data packet.


In Example 2, the subject matter of Example 1 can optionally include wherein when the processing circuitry processes the action, the processing circuitry is further configured to perform a Ternary CAM lookup on the portion of data and insert results from the lookup into the network packet as a pre-classification of the network data packet.


In Example 3, the subject matter of any of Examples 1-2 can optionally include wherein when the processing circuitry processes the action, the processing circuitry is further configured to decapsulate an outer tunnel encapsulated header of the network data packet, remove the tunnel encapsulated header from the network data packet, and add metadata into a header for the network data packet that indicates the tunnel encapsulated header was removed from the network data packet.


In Example 4, the subject matter of any of Examples 1-3 can optionally include wherein when the processing circuitry processes the action, the processing circuitry is further configured to copy a packet payload from the network data packet and send the copied packet payload to a location independent of locations that process the packet processing pipeline.


In Example 5, the subject matter of Examples 1˜4 can optionally include wherein when the processing circuitry processes the action, the processing circuitry is further configured to copy data packet forwarding rules from a first virtual switch to a second virtual switch.


In Example 6, the subject matter of any of Examples 1-5 can optionally include wherein the processing circuitry is a physical function integrated into the control device.


In Example 7, the subject matter of any of Examples 1-5 can optionally include wherein the processing circuitry is a virtualized function programmed into the control device.


In Example 8, the subject matter of any of Examples 1-7 can optionally include a data plane interface configured to forward the network data packet to one of: an OS kernel stack, a virtualized switch, and a device driver.


In Example 9, the subject matter of any of Examples 1-8 can optionally include wherein the control device is a network interface controller (NIC).


In Example 10, the subject matter of any of Examples 1-9 can optionally include wherein the control device is interfaced and integrated into a multi-core hardware server.


Example 11 includes subject matter such as a machine-readable medium including instructions that, when executed on a machine (such as a control device, interplane control device, control plane processor, computing device, NIC card, etc.) cause the machine to match a portion of a network data packet to an action in a match action table and accelerate processing of the network data packet through the machine by executing the action as part of a packet processing pipeline for the network data packet.


In Example 12, the subject matter of Example 11 may optionally include wherein the instructions to accelerate further include insert metadata into the network data packet indicating that a portion of the packet-processing pipeline was processed when the action was processed.


In Example 13, the subject matter of any of Examples 11-12 may optionally include wherein the instructions to accelerate further include instructions to copy a packet frame for the network data packet and send the copied packet frame to a location that is independent of locations associated with the packet-processing pipeline.


In Example 14, the subject matter of any of Examples 11-13 may optionally include wherein the instructions to accelerate further include instructions to assign the network data packet to a queue associated with a specific processing core of the machine.


In Example 15, the subject matter of Example 14 may optionally include wherein the instructions to accelerate further include instructions to filter the network data packet responsive to filtering rules.


In Example 16, the subject matter of Example 15 may optionally include wherein the instructions to accelerate further include instructions to set a resource identifier on the network data packet that identifies a resource for processing against the network data packet when processing the network data packet through the packet-processing pipeline.


Example 17 include a system (e.g., a server, computer, set of cooperating computers, etc.) a means for matching a portion of a network packet to an action and a means for processing the action to accelerate a packet-processing pipeline associated with the network packet based on processing the action.


Example 18 includes the subject matter of Example 17, and optionally wherein the network packet is sent from a first Virtual Machine (VM) to a second VM, and each of the VMs executing on a same multi-core server that executes the means for matching and the means for processing.


Example 19 includes subject matter of any of the Examples 16-17, and optionally further including a means for configuring the means for processing to custom-define the action.


In Example 20, the subject matter of any of the Examples 16-17, and optionally includes wherein the means for processing is one of: a virtualized network switch, a hardware switch, a kernel process, a device driver, and a virtualized interface.


The above detailed description includes references to the accompanying drawings, which form a part of the detailed description. The drawings show, by way of illustration, specific embodiments that may be practiced. These embodiments are also referred to herein as “examples.” Such examples may include elements in addition to those shown or described. However, also contemplated are examples that include the elements shown or described. Moreover, also contemplate are examples using any combination or permutation of those elements shown or described (or one or more aspects thereof), either with respect to a particular example (or one or more aspects thereof), or with respect to other examples (or one or more aspects thereof) shown or described herein.


Publications, patents, and patent documents referred to in this document are incorporated by reference herein in their entirety, as though individually incorporated by reference. In the event of inconsistent usages between this document and those documents so incorporated by reference, the usage in the incorporated reference(s) are supplementary to that of this document; for irreconcilable inconsistencies, the usage in this document controls.


In this document, the terms “a” or “an” are used, as is common in patent documents, to include one or more than one, independent of any other instances or usages of “at least one” or “one or more.” In this document, the term “or” is used to refer to a nonexclusive or, such that “A or B” includes “A but not B,” “B but not A,” and “A and B,” unless otherwise indicated. In the appended claims, the terms “including” and “in which” are used as the plain-English equivalents of the respective terms “comprising” and “wherein.” Also, in the following claims, the terms “including” and “comprising” are open-ended, that is, a system, device, article, or process that includes elements in addition to those listed after such a term in a claim are still deemed to fall within the scope of that claim. Moreover, in the following claims, the terms “first,” “second,” and “third,” etc. are used merely as labels, and are not intended to suggest a numerical order for their objects.


The above description is intended to be illustrative, and not restrictive. For example, the above-described examples (or one or more aspects thereof) may be used in combination with others. Other embodiments may be used, such as by one of ordinary skill in the art upon reviewing the above description. The Abstract is to allow the reader to quickly ascertain the nature of the technical disclosure and is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. Also, in the above Detailed Description, various features may be grouped together to streamline the disclosure. However, the claims may not set forth features disclosed herein because embodiments may include a subset of said features. Further, embodiments may include fewer features than those disclosed in a particular example. Thus, the following claims are hereby incorporated into the Detailed Description, with a claim standing on its own as a separate embodiment. The scope of the embodiments disclosed herein is to be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled.

Claims
  • 1. An apparatus usable in association with Ternary Content-Addressable Memory (TCAM) and at least one network, the apparatus being for use in network switch forwarding operations associated with the at least one network, the apparatus comprising: packet processing circuitry configurable to implement, when the apparatus is in operation, at least one programmable match-action operation associated with a packet processing pipeline, the at least one programmable match-action operation being to match, based upon match-action table lookup data, at least one action with received packet data, the at least one action to be carried out by the apparatus with respect to at least one portion of at least one packet containing the received packet data, the at least one action being for use in implementation of, at least in part, one or more of the network switch forwarding operations, the at least one action being configurable to comprise: packet data classification; andtunnel header data decapsulation associated with the received packet data; andprogrammable parser circuitry for use in association with a parse tree, the programmable parser circuitry to recognize header field data of the received packet data for use in the at least one programmable match-action operation;wherein: the programmable parser circuitry and the programmable packet processing circuitry are programmable, when the apparatus is in the operation, (1) based upon received configuration data and (2) via at least one application programming interface, the received configuration data to be generated based upon programming language instructions that define the match-action table lookup data and header format data of the received packet data;the apparatus is programmable to access at least one portion of the match-action table lookup data that is stored in the TCAM for use in the at least one programmable match-action operation; andthe packet data classification is based, at least in part, upon tunnel header field data classification rules.
  • 2. The apparatus of claim 1, wherein: the tunnel header data decapsulation comprises removal of one or more of: virtual extensible local area network (VXLAN) header data;network virtualization using generic routing extension (NV-GRE) header data;generic network virtualization encapsulation (GENEVE) header data; and/ormultiprotocol label switch (MPLS) header data.
  • 3. The apparatus of claim 2, wherein: the match-action table lookup data comprises the tunnel header field data classification rules.
  • 4. The apparatus of claim 3, wherein: the apparatus is comprised in a network switch; andthe network switch comprises physical ports.
  • 5. The apparatus of claim 4, wherein: the match-action table lookup data also comprises access control list data.
  • 6. At least one non-transitory machine-readable medium storing instructions for being executed by an apparatus, the apparatus being usable in association with Ternary Content-Addressable Memory (TCAM) and at least one network, the apparatus being for use in network switch forwarding operations associated with the at least one network, the apparatus comprising packet processing circuitry and programmable parser circuitry, the instructions when executed by the apparatus resulting in the apparatus being configured for performance of operations comprising: implementing, by the packet processing circuitry, at least one programmable match-action operation associated with a packet processing pipeline, the at least one programmable match-action operation being to match, based upon match-action table lookup data, at least one action with received packet data, the at least one action to be carried out by the apparatus with respect to at least one portion of at least one packet containing the received packet data, the at least one action being for use in implementation of, at least in part, one or more of the network switch forwarding operations, the at least one action being configurable to comprise: packet data classification; andtunnel header data decapsulation associated with the received packet data; andrecognizing, by the programmable parser circuitry, header field data of the received packet data for use in the at least one programmable match-action operation, the programmable parser circuitry being for use in association with a parse tree;wherein: the programmable parser circuitry and the programmable packet processing circuitry are programmable, when the apparatus is in the operation, (1) based upon received configuration data and (2) via at least one application programming interface, the received configuration data to be generated based upon programming language instructions that define the match-action table lookup data and header format data of the received packet data;the apparatus is programmable to access at least one portion of the match-action table lookup data that is stored in the TCAM for use in the at least one programmable match-action operation; andthe packet data classification is based, at least in part, upon tunnel header field data classification rules.
  • 7. The at least one non-transitory machine-readable medium of claim 6, wherein: the tunnel header data decapsulation comprises removal of one or more of: virtual extensible local area network (VXLAN) header data;network virtualization using generic routing extension (NV-GRE) header data;generic network virtualization encapsulation (GENEVE) header data; and/ormultiprotocol label switch (MPLS) header data.
  • 8. The at least one non-transitory machine-readable medium of claim 7, wherein: the match-action table lookup data comprises the tunnel header field data classification rules.
  • 9. The at least one non-transitory machine-readable medium of claim 8, wherein: the apparatus is comprised in a network switch; andthe network switch comprises physical ports.
  • 10. The at least one non-transitory machine-readable medium of claim 9, wherein: the match-action table lookup data also comprises access control list data.
  • 11. A method implemented using an apparatus, the apparatus being usable in association with Ternary Content-Addressable Memory (TCAM) and at least one network, the apparatus being for use in network switch forwarding operations associated with the at least one network, the apparatus comprising packet processing circuitry and programmable parser circuitry, the method comprising: implementing, by the packet processing circuitry, at least one programmable match-action operation associated with a packet processing pipeline, the at least one programmable match-action operation being to match, based upon match-action table lookup data, at least one action with received packet data, the at least one action to be carried out by the apparatus with respect to at least one portion of at least one packet containing the received packet data, the at least one action being for use in implementation of, at least in part, one or more of the network switch forwarding operations, the at least one action being configurable to comprise: packet data classification; andtunnel header data decapsulation associated with the received packet data; andrecognizing, by the programmable parser circuitry, header field data of the received packet data for use in the at least one programmable match-action operation, the programmable parser circuitry being for use in association with a parse tree;wherein: the programmable parser circuitry and the programmable packet processing circuitry are programmable, when the apparatus is in the operation, (1) based upon received configuration data and (2) via at least one application programming interface, the received configuration data to be generated based upon programming language instructions that define the match-action table lookup data and header format data of the received packet data;the apparatus is programmable to access at least one portion of the match-action table lookup data that is stored in the TCAM for use in the at least one programmable match-action operation; andthe packet data classification is based, at least in part, upon tunnel header field data classification rules.
  • 12. The method of claim 11, wherein: the tunnel header data decapsulation comprises removal of one or more of: virtual extensible local area network (VXLAN) header data;network virtualization using generic routing extension (NV-GRE) header data;generic network virtualization encapsulation (GENEVE) header data; and/ormultiprotocol label switch (MPLS) header data.
  • 13. The method of claim 12, wherein: the match-action table lookup data comprises the tunnel header field data classification rules.
  • 14. The method of claim 13, wherein: the apparatus is comprised in a network switch; andthe network switch comprises physical ports.
  • 15. The method of claim 14, wherein: the match-action table lookup data also comprises access control list data.
  • 16. Network switch circuitry usable in association with Ternary Content-Addressable Memory (TCAM) and at least one network, the network switch circuitry being for use in network switch forwarding operations associated with the at least one network, the network switch circuitry comprising: physical ports;packet processing circuitry configurable to implement, when the network switch circuitry is in operation, at least one programmable match-action operation associated with a packet processing pipeline, the at least one programmable match-action operation being to match, based upon match-action table lookup data, at least one action with received packet data, the at least one action to be carried out by the network switch circuitry with respect to at least one portion of at least one packet containing the received packet data, the at least one action being for use in implementation of, at least in part, one or more of the network switch forwarding operations that are to be carried out using the physical ports, the at least one action being configurable to comprise: packet data classification; andtunnel header data decapsulation associated with the received packet data; andprogrammable parser circuitry for use in association with a parse tree, the programmable parser circuitry to recognize header field data of the received packet data for use in the at least one programmable match-action operation;wherein: the programmable parser circuitry and the programmable packet processing circuitry are programmable, when the network switch circuitry is in the operation, (1) based upon received configuration data and (2) via at least one application programming interface, the received configuration data to be generated based upon programming language instructions that define the match-action table lookup data and header format data of the received packet data;the network switch circuitry is programmable to access at least one portion of the match-action table lookup data that is stored in the TCAM for use in the at least one programmable match-action operation; andthe packet data classification is based, at least in part, upon tunnel header field data classification rules.
  • 17. The network switch circuitry of claim 16, wherein: the tunnel header data decapsulation comprises removal of one or more of: virtual extensible local area network (VXLAN) header data;network virtualization using generic routing extension (NV-GRE) header data;generic network virtualization encapsulation (GENEVE) header data; and/ormultiprotocol label switch (MPLS) header data.
  • 18. The network switch circuitry of claim 17, wherein: the match-action table lookup data comprises the tunnel header field data classification rules.
  • 19. The network switch circuitry of claim 18, wherein: the match-action table lookup data also comprises access control list data.
CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation (and claims the benefit of priority under 35 U.S.C. § 120) of U.S. application Ser. No. 16/460,798, filed Jul. 2, 2019, and entitled ACCELERATED NETWORK PACKET PROCESSING, which application is a continuation (and claims the benefit of priority under 35 U.S.C. § 120) of U.S. application Ser. No. 15/878,966, filed Jan. 24, 2018, and entitled ACCELERATED NETWORK PACKET PROCESSING, which application is a continuation (and claims the benefit of priority under 35 U.S.C. § 120) of U.S. application Ser. No. 14/977,810, filed Dec. 22, 2015 and entitled ACCELERATED NETWORK PACKET PROCESSING. The disclosures of the prior Applications are considered part of and are hereby incorporated by reference in their entirety in the disclosure of this Application.

US Referenced Citations (333)
Number Name Date Kind
5243596 Port et al. Sep 1993 A
5642483 Topper Jun 1997 A
5784003 Dahlgren Jul 1998 A
6157955 Narad et al. Dec 2000 A
6442172 Wallner et al. Aug 2002 B1
6453360 Muller et al. Sep 2002 B1
6735679 Herbst et al. May 2004 B1
6836483 Lee Dec 2004 B1
6948099 Tallam Sep 2005 B1
6976149 Brandt et al. Dec 2005 B1
6980552 Belz et al. Dec 2005 B1
7042888 Berggreen May 2006 B2
7046685 Matsuoka et al. May 2006 B1
7062571 Dale et al. Jun 2006 B1
7177276 Epps et al. Feb 2007 B1
7203740 Putzolu et al. Apr 2007 B1
7389462 Wang Jun 2008 B1
7492714 Liao et al. Feb 2009 B1
7499941 Michaeli et al. Mar 2009 B2
7539777 Aitken May 2009 B1
7633880 Bang et al. Dec 2009 B2
7643486 Belz et al. Jan 2010 B2
7826470 Aloni et al. Nov 2010 B1
7872774 Okamoto Jan 2011 B2
7873959 Zhu et al. Jan 2011 B2
7889750 Parker Feb 2011 B1
7904642 Gupta et al. Mar 2011 B1
7961734 Panwar et al. Jun 2011 B2
8077611 Bettink et al. Dec 2011 B2
8094659 Arad Jan 2012 B1
8155135 Aloni et al. Apr 2012 B2
8514855 Robertson et al. Aug 2013 B1
8527613 Malone et al. Sep 2013 B2
8593955 Yano et al. Nov 2013 B2
8638793 Ben-Mayor et al. Jan 2014 B1
8693374 Murphy et al. Apr 2014 B1
8738860 Griffin et al. May 2014 B1
8788512 Asaad et al. Jul 2014 B2
8798047 Wadekar et al. Aug 2014 B1
8971338 Mishra et al. Mar 2015 B2
9049153 Casado et al. Jun 2015 B2
9049271 Hobbs et al. Jun 2015 B1
9055004 Edsall et al. Jun 2015 B2
9055114 Talaski et al. Jun 2015 B1
9112818 Arad et al. Aug 2015 B1
9124644 Patel et al. Sep 2015 B2
9213537 Bandakka et al. Dec 2015 B2
9258224 Bosshart et al. Feb 2016 B2
9276846 Goyal et al. Mar 2016 B2
9294386 Narad Mar 2016 B2
9298446 Chang et al. Mar 2016 B2
9450817 Bahadur et al. Sep 2016 B1
9467363 Gao Oct 2016 B2
9544231 Bosshart et al. Jan 2017 B2
9590925 Yamashita Mar 2017 B2
9686209 Arad et al. Jun 2017 B1
9692690 Joshi et al. Jun 2017 B2
9712439 Bosshart et al. Jul 2017 B2
9755932 Godbole et al. Sep 2017 B1
9825862 Bosshart Nov 2017 B2
9826071 Bosshart Nov 2017 B2
9838268 Mattson Dec 2017 B1
9888033 Li et al. Feb 2018 B1
9891898 Tonsing Feb 2018 B1
9912610 Bosshart et al. Mar 2018 B2
9912774 Daly et al. Mar 2018 B2
9923816 Kim et al. Mar 2018 B2
9960956 Johnson et al. May 2018 B1
10009276 Bosshart et al. Jun 2018 B2
10015048 Gasparakis et al. Jul 2018 B2
10044646 Detwiler Aug 2018 B1
10091137 Tran et al. Oct 2018 B2
10135734 Singh et al. Nov 2018 B1
10225381 Bosshart Mar 2019 B1
10291555 K et al. May 2019 B2
10341242 Srinivasan et al. Jul 2019 B2
10361914 Gasparakis et al. Jul 2019 B2
10412018 Feng et al. Sep 2019 B1
10419242 Tonsing et al. Sep 2019 B1
10419366 Kim et al. Sep 2019 B1
10432527 Bosshart Oct 2019 B1
10686735 Watson Jun 2020 B1
10892939 Castaldelli et al. Jan 2021 B2
20010043611 Kadambi et al. Nov 2001 A1
20020001356 Shenoi Jan 2002 A1
20020136163 Kawakami et al. Sep 2002 A1
20020172210 Wolrich et al. Nov 2002 A1
20030009466 Ta et al. Jan 2003 A1
20030043825 Magnussen et al. Mar 2003 A1
20030046414 Pettyjohn et al. Mar 2003 A1
20030046429 Sonksen Mar 2003 A1
20030063345 Fossum et al. Apr 2003 A1
20030107996 Black et al. Jun 2003 A1
20030118022 Kulkarni et al. Jun 2003 A1
20030147401 Kyronaho et al. Aug 2003 A1
20030154358 Seong et al. Aug 2003 A1
20030167373 Winters et al. Sep 2003 A1
20030219026 Sun et al. Nov 2003 A1
20040024894 Osman et al. Feb 2004 A1
20040031029 Lee et al. Feb 2004 A1
20040042477 Bitar et al. Mar 2004 A1
20040105384 Gallezot et al. Jun 2004 A1
20040123220 Johnson et al. Jun 2004 A1
20040165588 Pandya Aug 2004 A1
20040213156 Smallwood et al. Oct 2004 A1
20050013251 Wang et al. Jan 2005 A1
20050041590 Olakangil et al. Feb 2005 A1
20050060428 Corl et al. Mar 2005 A1
20050078651 Lee et al. Apr 2005 A1
20050086353 Shirakawa et al. Apr 2005 A1
20050108518 Pandya May 2005 A1
20050120173 Minowa Jun 2005 A1
20050129059 Jiang et al. Jun 2005 A1
20050135399 Baden et al. Jun 2005 A1
20050149823 Lee Jul 2005 A1
20050198531 Kaniz et al. Sep 2005 A1
20050243852 Bitar et al. Nov 2005 A1
20060002386 Yik et al. Jan 2006 A1
20060039374 Belz et al. Feb 2006 A1
20060050690 Epps et al. Mar 2006 A1
20060072480 Deval et al. Apr 2006 A1
20060092857 Ansari et al. May 2006 A1
20060114895 Anand et al. Jun 2006 A1
20060114914 Anand et al. Jun 2006 A1
20060117126 Leung et al. Jun 2006 A1
20060174242 Zhu et al. Aug 2006 A1
20060277346 Doak et al. Dec 2006 A1
20070008985 Lakshmanamurthy et al. Jan 2007 A1
20070050426 Dubal et al. Mar 2007 A1
20070055664 Michaeli et al. Mar 2007 A1
20070104102 Opsasnick May 2007 A1
20070104211 Opsasnick May 2007 A1
20070153796 Kesavan et al. Jul 2007 A1
20070195761 Tatar et al. Aug 2007 A1
20070195773 Tatar et al. Aug 2007 A1
20070208876 Davis Sep 2007 A1
20070230493 Dravida et al. Oct 2007 A1
20070236734 Okamoto Oct 2007 A1
20070280277 Lund et al. Dec 2007 A1
20080082792 Vincent et al. Apr 2008 A1
20080130670 Kim et al. Jun 2008 A1
20080144662 Marcondes et al. Jun 2008 A1
20080175449 Fang et al. Jul 2008 A1
20080285571 Arulambalam et al. Nov 2008 A1
20090006605 Chen et al. Jan 2009 A1
20090096797 Du et al. Apr 2009 A1
20090106523 Steiss Apr 2009 A1
20090147787 Arulambalam et al. Jun 2009 A1
20090180475 Hashimoto Jul 2009 A1
20100085891 Kind et al. Apr 2010 A1
20100128735 Lipschutz May 2010 A1
20100135158 Adams Jun 2010 A1
20100140364 Nordberg et al. Jun 2010 A1
20100145475 Bartels et al. Jun 2010 A1
20100150164 Ma Jun 2010 A1
20100182920 Matsuoka Jul 2010 A1
20100191951 Malone et al. Jul 2010 A1
20100228733 Harrison et al. Sep 2010 A1
20100238812 Boutros et al. Sep 2010 A1
20100312941 Aloni et al. Dec 2010 A1
20110149960 Gutierrez Jun 2011 A1
20120033550 Yano et al. Feb 2012 A1
20120159235 Suganthi et al. Jun 2012 A1
20120170585 Mehra et al. Jul 2012 A1
20120173661 Mahaffey et al. Jul 2012 A1
20120177047 Roitshtein Jul 2012 A1
20120284438 Zievers Nov 2012 A1
20130003556 Boden et al. Jan 2013 A1
20130028265 Ronchetti et al. Jan 2013 A1
20130100951 Ishizuka Apr 2013 A1
20130108264 Deruijter et al. May 2013 A1
20130124491 Pepper et al. May 2013 A1
20130163426 Beliveau et al. Jun 2013 A1
20130163427 Beliveau et al. Jun 2013 A1
20130163475 Beliveau et al. Jun 2013 A1
20130166703 Hammer et al. Jun 2013 A1
20130227051 Khakpour et al. Aug 2013 A1
20130227519 Maleport Aug 2013 A1
20130290622 Dey et al. Oct 2013 A1
20130315054 Shamis et al. Nov 2013 A1
20130318107 Asaad et al. Nov 2013 A1
20130346814 Zadigian et al. Dec 2013 A1
20140033489 Kawashima Feb 2014 A1
20140040527 Kanigicheria et al. Feb 2014 A1
20140043974 Kwan et al. Feb 2014 A1
20140082302 Rommelmann et al. Mar 2014 A1
20140115571 Wang et al. Apr 2014 A1
20140115666 Garcia Morchon et al. Apr 2014 A1
20140140342 Narad May 2014 A1
20140181232 Manula et al. Jun 2014 A1
20140181818 Vincent et al. Jun 2014 A1
20140204943 Palmer Jul 2014 A1
20140233568 Dong Aug 2014 A1
20140241358 Bosshart et al. Aug 2014 A1
20140241359 Bosshart et al. Aug 2014 A1
20140241361 Bosshart et al. Aug 2014 A1
20140241362 Bosshart et al. Aug 2014 A1
20140244966 Bosshart et al. Aug 2014 A1
20140269432 Goyal et al. Sep 2014 A1
20140301192 Lee et al. Oct 2014 A1
20140321473 Chen et al. Oct 2014 A1
20140321476 Yu et al. Oct 2014 A1
20140328180 Kim et al. Nov 2014 A1
20140328344 Bosshart Nov 2014 A1
20140334489 Bosshart et al. Nov 2014 A1
20150003259 Gao Jan 2015 A1
20150009796 Koponen et al. Jan 2015 A1
20150010000 Zhang et al. Jan 2015 A1
20150020060 Bandakka et al. Jan 2015 A1
20150023147 Lee et al. Jan 2015 A1
20150043589 Han et al. Feb 2015 A1
20150081833 Pettit et al. Mar 2015 A1
20150092539 Sivabalan et al. Apr 2015 A1
20150109913 Yamashita Apr 2015 A1
20150110114 Wohlgemuth et al. Apr 2015 A1
20150121355 Chang et al. Apr 2015 A1
20150131666 Kang et al. May 2015 A1
20150131667 Ko et al. May 2015 A1
20150142932 Hallivuori et al. May 2015 A1
20150142991 Zaloom May 2015 A1
20150146527 Kishore et al. May 2015 A1
20150156288 Lu et al. Jun 2015 A1
20150172198 Levy et al. Jun 2015 A1
20150178395 Tiwari et al. Jun 2015 A1
20150180769 Wang et al. Jun 2015 A1
20150194215 Douglas et al. Jul 2015 A1
20150195206 Memon et al. Jul 2015 A1
20150222560 Kakadia et al. Aug 2015 A1
20150249572 Mack-Crane et al. Sep 2015 A1
20150256465 Mack-Crane et al. Sep 2015 A1
20150271011 Neginhal et al. Sep 2015 A1
20150281125 Koponen et al. Oct 2015 A1
20150319086 Tripathi et al. Nov 2015 A1
20150363522 Maurya Dec 2015 A1
20150381418 Fausak et al. Dec 2015 A1
20150381495 Cherian et al. Dec 2015 A1
20160006654 Fernando et al. Jan 2016 A1
20160014073 Reddy et al. Jan 2016 A1
20160019161 Patel et al. Jan 2016 A1
20160094460 Shelar et al. Mar 2016 A1
20160139892 Atreya et al. May 2016 A1
20160149784 Zhang et al. May 2016 A1
20160156557 Bosshart et al. Jun 2016 A1
20160173371 Bays Jun 2016 A1
20160173383 Liu et al. Jun 2016 A1
20160188313 Dubal et al. Jun 2016 A1
20160188320 Chang et al. Jun 2016 A1
20160191306 Gasparakis et al. Jun 2016 A1
20160191361 Behera et al. Jun 2016 A1
20160191370 Wood Jun 2016 A1
20160191384 Shelar et al. Jun 2016 A1
20160191406 Xiao et al. Jun 2016 A1
20160197852 Hutchison et al. Jul 2016 A1
20160212012 Young et al. Jul 2016 A1
20160232019 Shah et al. Aug 2016 A1
20160234067 Dolganow et al. Aug 2016 A1
20160234097 Chang Aug 2016 A1
20160234102 Kotalwar et al. Aug 2016 A1
20160234103 Kotalwar et al. Aug 2016 A1
20160241459 Zheng et al. Aug 2016 A1
20160301601 Anand et al. Oct 2016 A1
20160315866 Thapar et al. Oct 2016 A1
20160323243 Levasseur et al. Nov 2016 A1
20160330127 Kim et al. Nov 2016 A1
20160330128 Wang Nov 2016 A1
20160337329 Sood et al. Nov 2016 A1
20160342510 Pani Nov 2016 A1
20160344629 Gray Nov 2016 A1
20160357534 Krishnamoorthi et al. Dec 2016 A1
20160359685 Yadav et al. Dec 2016 A1
20170005951 Labonte et al. Jan 2017 A1
20170013452 Mentze et al. Jan 2017 A1
20170019302 Lapiotis et al. Jan 2017 A1
20170019329 Kozat et al. Jan 2017 A1
20170034082 Pfaff Feb 2017 A1
20170041209 Joshi et al. Feb 2017 A1
20170048144 Liu Feb 2017 A1
20170053012 Levy et al. Feb 2017 A1
20170063690 Bosshart Mar 2017 A1
20170064047 Bosshart Mar 2017 A1
20170070416 Narayanan et al. Mar 2017 A1
20170075692 Naresh et al. Mar 2017 A1
20170085414 Castaldelli et al. Mar 2017 A1
20170085477 Li et al. Mar 2017 A1
20170085479 Wang et al. Mar 2017 A1
20170091258 Rajahalme Mar 2017 A1
20170093707 Kim et al. Mar 2017 A1
20170093986 Kim et al. Mar 2017 A1
20170093987 Kaushalram et al. Mar 2017 A1
20170111275 Li et al. Apr 2017 A1
20170118041 Bhattacharya et al. Apr 2017 A1
20170118042 Bhattacharya et al. Apr 2017 A1
20170126588 Anand et al. May 2017 A1
20170134282 Agarwal et al. May 2017 A1
20170134310 Koladi et al. May 2017 A1
20170142000 Cai et al. May 2017 A1
20170142011 Zhang et al. May 2017 A1
20170149632 Saltsidis et al. May 2017 A1
20170180273 Daly et al. Jun 2017 A1
20170195229 Ulas et al. Jul 2017 A1
20170208015 Volkening et al. Jul 2017 A1
20170220499 Gray Aug 2017 A1
20170222881 Holbrook et al. Aug 2017 A1
20170223575 Duda et al. Aug 2017 A1
20170251077 Eerpini et al. Aug 2017 A1
20170264571 Aibester et al. Sep 2017 A1
20170289034 Bosshart et al. Oct 2017 A1
20180006945 Flajslik et al. Jan 2018 A1
20180006950 Flajslik et al. Jan 2018 A1
20180054385 Dharmapurikar et al. Feb 2018 A1
20180103060 Li et al. Apr 2018 A1
20180115478 Kim et al. Apr 2018 A1
20180124183 Kozat et al. May 2018 A1
20180191640 Calderon et al. Jul 2018 A1
20180234340 Kim et al. Aug 2018 A1
20180234355 Kim et al. Aug 2018 A1
20180262424 Roeland et al. Sep 2018 A1
20180287819 Mayer-Wolf et al. Oct 2018 A1
20180316549 Gasparakis et al. Nov 2018 A1
20180375755 Joshi et al. Dec 2018 A1
20190394086 Gasparakis et al. Dec 2019 A1
20200007473 Kim et al. Jan 2020 A1
20200021486 Gasparakis et al. Jan 2020 A1
20200076737 Bosshart Mar 2020 A1
20200084093 Gasparakis et al. Mar 2020 A1
20200099617 Bosshart Mar 2020 A1
20200099618 Bosshart Mar 2020 A1
20200099619 Bosshart Mar 2020 A1
20200228433 Lee Jul 2020 A1
20200244576 Wetterwald et al. Jul 2020 A1
20200280428 Kovacs et al. Sep 2020 A1
20200280518 Lee et al. Sep 2020 A1
20220091992 Shanbhogue et al. Mar 2022 A1
Foreign Referenced Citations (7)
Number Date Country
1589551 Mar 2005 CN
101352012 Jan 2009 CN
3229424 Oct 2017 EP
2015080175 Apr 2015 JP
6127900 May 2017 JP
2013101024 Jul 2013 WO
2017112165 Jun 2017 WO
Non-Patent Literature Citations (137)
Entry
Notice of Allowance for Chinese Patent Application No. 201680075637.4, dated Apr. 1, 2022.
Notice of Allowance for U.S. Appl. No. 16/519,873, dated Mar. 17, 2022.
Notice of Allowance for U.S. Appl. No. 16/582,798, dated Mar. 22, 2022.
Notice of Allowance for U.S. Appl. No. 16/687,271, dated Mar. 22, 2022.
Notice of Allowance for U.S. Appl. No. 16/519,873, dated Dec. 3, 2021.
Notice of Allowance for U.S. Appl. No. 16/582,798, dated Dec. 1, 2021.
Notice of Allowance for U.S. Appl. No. 16/687,271, dated Dec. 1, 2021.
Office Action and English Translation of Office Action for Chinese Patent Application No. 201680075637.4, dated Jan. 5, 2022.
Advisory Action for U.S. Appl. No. 15/835,238, dated Nov. 22, 2019, 3 pages.
Advisory Action for U.S. Appl. No. 15/888,054, dated Feb. 10, 2020.
Final Office Action for U.S. Appl. No. 15/784,191, dated Feb. 26, 2021.
Final Office Action for U.S. Appl. No. 15/784,191, dated May 7, 2020, 13 pages.
Final Office Action for U.S. Appl. No. 15/784,192, dated Jun. 1, 2020, 14 pages.
Final Office Action for U.S. Appl. No. 15/888,050, dated Dec. 12, 2019.
Final Office Action for U.S. Appl. No. 15/888,054, dated Sep. 18, 2019.
International Preliminary Report on Patentability for PCT Application No. PCT/US2016/062511, dated Jun. 26, 2018.
International Search Report and Written Opinion for PCT Application No. PCT/US2016/062511, dated Feb. 28, 2017.
Non-Final Office Action for U.S. Appl. No. 16/569,554, dated Feb. 19, 2021.
Notice of Allowance for U.S. Appl. No. 14/583,664, dated Feb. 28, 2018.
Notice of Allowance for U.S. Appl. No. 14/836,850, dated Jun. 20, 2017.
Notice of Allowance for U.S. Appl. No. 14/836,855, dated Jun. 30, 2017.
Notice of Allowance for U.S. Appl. No. 14/977,810, dated Oct. 20, 2017.
Notice of Allowance for U.S. Appl. No. 15/678,549, dated Apr. 8, 2020.
Notice of Allowance for U.S. Appl. No. 15/678,549, dated Dec. 27, 2019.
Notice of Allowance for U.S. Appl. No. 15/678,556, dated Feb. 4, 2020.
Notice of Allowance for U.S. Appl. No. 15/729,555, dated May 2, 2019.
Notice of Allowance for U.S. Appl. No. 15/729,593, dated Nov. 15, 2018.
Notice of Allowance for U.S. Appl. No. 15/784,190, dated May 10, 2019, 20 pages.
Notice of Allowance for U.S. Appl. No. 15/784,191, dated Apr. 19, 2019, 7 pages.
Notice of Allowance for U.S. Appl. No. 15/784,191, dated Aug. 21, 2019, 8 pages.
Notice of Allowance for U.S. Appl. No. 15/784,191, dated Aug. 31, 2021.
Notice of Allowance for U.S. Appl. No. 15/784,191, dated May 5, 2021.
Notice of Allowance for U.S. Appl. No. 15/784,192, dated Mar. 17, 2021.
Notice of Allowance for U.S. Appl. No. 15/784,192, dated Jun. 30, 2021.
Notice of Allowance for U.S. Appl. No. 15/784,192, dated Sep. 30, 2021.
Notice of Allowance for U.S. Appl. No. 15/835,233, dated Jul. 3, 2019, 8 pages.
Notice of Allowance for U.S. Appl. No. 15/835,233, dated Oct. 29, 2019.
Notice of Allowance for U.S. Appl. No. 15/835,235, dated Apr. 24, 2020.
Notice of Allowance for U.S. Appl. No. 15/835,235, dated Aug. 20, 2019, 16 pages.
Notice of Allowance for U.S. Appl. No. 15/835,238, dated Sep. 30, 2020.
Notice of Allowance for U.S. Appl. No. 15/835,239, dated Nov. 13, 2019, 10 pages.
Notice of Allowance for U.S. Appl. No. 15/835,242, dated Jun. 24, 2020.
Notice of Allowance for U.S. Appl. No. 15/835,242, dated Jul. 1, 2019, 7 pages.
Notice of Allowance for U.S. Appl. No. 15/835,249, dated Jul. 25, 2019.
Notice of Allowance for U.S. Appl. No. 15/835,250, dated Jul. 25, 2019, 17 pages.
Notice of Allowance for U.S. Appl. No. 15/878,966, dated May 15, 2019.
Notice of Allowance for U.S. Appl. No. 16/026,318, dated Mar. 12, 2019.
Notice of Allowance for U.S. Appl. No. 16/460,798, dated May 27, 2021.
Notice of Allowance for U.S. Appl. No. 16/519,873, dated Aug. 30, 2021.
Notice of Allowance for U.S. Appl. No. 16/582,798, dated Aug. 27, 2021.
Notice of Allowance for U.S. Appl. No. 16/687,271, dated Aug. 30, 2021.
Notice of Allowance for U.S. Appl. No. 16/789,339, dated Jul. 29, 2021.
Translation of Office Action for Chinese Patent Application No. 201680075637.4, dated Mar. 2, 2021.
Translation of Office Action for Chinese Patent Application No. 201680075637.4, dated Sep. 23, 2021.
Office Action for U.S. Appl. No. 15/878,966, dated Jan. 11, 2019.
Office Action for U.S. Appl. No. 14/583,664, dated Feb. 27, 2017.
Office Action for U.S. Appl. No. 14/583,664, dated Jul. 28, 2016.
Office Action for U.S. Appl. No. 14/583,664, dated Oct. 18, 2017.
Office Action for U.S. Appl. No. 14/863,961, dated Jun. 16, 2017.
Office Action for U.S. Appl. No. 14/864,032, dated Feb. 14, 2017.
Office Action for U.S. Appl. No. 14/977,810, dated Jun. 29, 2017.
Office Action for U.S. Appl. No. 15/678,549, dated Feb. 26, 2019.
Office Action for U.S. Appl. No. 15/678,549, dated Jul. 30, 2019.
Office Action for U.S. Appl. No. 15/678,556, dated Jun. 19, 2019.
Office Action for U.S. Appl. No. 15/678,565, dated Jun. 13, 2019.
Office Action for U.S. Appl. No. 15/729,593, dated Aug. 10, 2018.
Office Action for U.S. Appl. No. 15/784,191, dated Aug. 26, 2020, 14 pages.
Office Action for U.S. Appl. No. 15/784,191, dated Dec. 19, 2018, 11 pages.
Office Action for U.S. Appl. No. 15/784,191, dated Jan. 24, 2020, 12 pages.
Office Action for U.S. Appl. No. 15/784,192, dated Sep. 19, 2019, 14 pages.
Office Action for U.S. Appl. No. 15/835,233, dated Feb. 8, 2019, 17 pages.
Office Action for U.S. Appl. No. 15/835,235, dated Feb. 25, 2019, 24 pages.
Office Action for U.S. Appl. No. 15/835,238, dated Dec. 11, 2019.
Office Action for U.S. Appl. No. 15/835,238, dated Feb. 7, 2019.
Office Action for U.S. Appl. No. 15/835,238, dated Jun. 19, 2019.
Office Action for U.S. Appl. No. 15/835,238, dated Jun. 5, 2020.
Office Action for U.S. Appl. No. 15/835,239, dated Feb. 7, 2019, 20 pages.
Office Action for U.S. Appl. No. 15/835,239, dated Jun. 19, 2019, 18 pages.
Office Action for U.S. Appl. No. 15/835,242, dated Oct. 18, 2019, 8 pages.
Office Action for U.S. Appl. No. 15/835,247, dated Dec. 31, 2018, 18 pages.
Office Action for U.S. Appl. No. 15/835,247, dated Jul. 10, 2019.
Office Action for U.S. Appl. No. 15/835,249, dated Dec. 31, 2018.
Office Action for U.S. Appl. No. 15/835,250, dated Apr. 4, 2019, 16 pages.
Office Action for U.S. Appl. No. 15/835,393, dated Jan. 7, 2021.
Office Action for U.S. Appl. No. 15/888,050, dated Jun. 11, 2019.
Office Action for U.S. Appl. No. 15/888,054, dated Mar. 11, 2019.
Office Action for U.S. Appl. No. 16/026,318, dated Sep. 20, 2018.
Office Action for U.S. Appl. No. 16/288,074, dated Oct. 7, 2020.
Office Action for U.S. Appl. No. 16/288,074, dated Mar. 5, 2020.
Office Action for U.S. Appl. No. 16/4670,798, dated Nov. 18, 2020.
Office Action for U.S. Appl. No. 16/519,873, dated Jun. 11, 2021.
Office Action for U.S. Appl. No. 16/569,554, dated Aug. 18, 2020.
Office Action for U.S. Appl. No. 16/569,554, dated Jul. 2, 2021.
Office Action for U.S. Appl. No. 16/569,554, dated Sep. 27, 2021.
Office Action for U.S. Appl. No. 16/573,847 dated Jan. 6, 2021.
Office Action for U.S. Appl. No. 16/573,847, dated Aug. 2, 2021.
Office Action for U.S. Appl. No. 16/582,798, dated Jun. 24, 2021.
Office Action for U.S. Appl. No. 16/687,271, dated Jun. 24, 2021.
Office Action for U.S. Appl. No. 16/695,044, dated Jul. 8, 2021.
Office Action for U.S. Appl. No. 16/695,049, dated Jul. 21, 2021.
Office Action for U.S. Appl. No. 16/695,053 dated Aug. 4, 2021.
Office Action for U.S. Appl. No. 16/804,960, dated Aug. 19, 2021.
Office Action for U.S. Appl. No. 16/804,960, dated May 12, 2021.
“Selection of Cyclic Redundancy Code and Checksum Algorithms to Ensure Critical Data Integrity”, Federal Aviation Administration William J. Hughes Technical Center Aviation Research Division Atlantic City International Airport New Jersey 08405, DOT/FAA/TC-14/49, Mar. 2015, 111 pages.
Arashloo, Mina Tahmasbi , et al. , “SNAP: Stateful Network-Wide Abstractions for Packet Processing”, SIGCOMM '16, Aug. 22-26, 2016, 27 pages, ACM, Florianopolis, Brazil.
Bosshart, P. , et al. , “Forwarding Metamorphosis: Fast Programmable Match-Action Processing in Hardware or SDN”, SIGCOMM'13, Aug. 12-16, 2013, 12 pages, ACM, Hong Kong, China.
Kaufmann, A. , et al. , “High Performance Packet Processing with FlexNIC”, ASPLOS'16, Apr. 2-6, 2016, 15 pages, ACM, Atlanta, GA, USA.
Moshref, Masoud , et al. , “Flow-level State Transition as a New Switch Primitive for SON” HotSDN'14, Aug. 22, 2014, 6 pages, ACM, Chicago, IL, USA.
Sivaraman, A. , et al. , “Towards Programmable Packet Scheduling”, HotNets'15, Nov. 16-17, 2015, 7 pages, ACM, Philadelphia, PA, USA.
Sivaraman, Anirudh , et al. , “Packet Transactions: a Programming Model for Data-Plane Algorithms at Hardware Speed”, arXiv:1512.05023v1, Dec. 16, 2015, 22 pages.
Sivaraman, Anirudh , et al. , “Packet Transactions: High-level Programming for Line-Rate Switches”, ?rXiv:1512.05023v2, Jan. 30, 2016, 16 pages.
Sivaraman, Anirudh , et al. , “Packet Transactions: High-level Programming for Line-Rate Switches”, SIGCOMM'16, Aug. 22-26, 2016, 14 pages, ACM, Florianopolis, Brazil.
Sivaraman, Anirudh , et al. , “Programmable Packet Scheduling at Line Rate”, SIGCOMM'16, Aug. 22-26, 2016, 14 pages, ACM, Florianopolis, Brazil.
Song, “Protocol-Oblivious Forwarding: Unleashe the Power of SDN through a Future-Proof Forwarding Plane”, Huawei Technologies, USA, 6 pages.
Final Office Action for U.S. Appl. No. 16/695,044, dated Dec. 20, 2021.
Final Office Action for U.S. Appl. No. 16/804,960, dated Apr. 14, 2022.
Final Office Action for U.S. Appl. No. 17/134,110 dated Oct. 25, 2022.
Notice of Allowance for U.S. Appl. No. 15/835,247 dated Dec. 29, 2021.
Notice of Allowance for U.S. Appl. No. 15/835,247, dated Apr. 7, 2022.
Notice of Allowance for U.S. Appl. No. 15/835,247, dated Jul. 29, 2022.
Notice of Allowance for U.S. Appl. No. 16/573,847, dated Apr. 14, 2022.
Notice of Allowance for U.S. Appl. No. 16/573,847, dated Dec. 15, 2021.
Notice of Allowance for U.S. Appl. No. 16/695,044 dated Apr. 27, 2022.
Notice of Allowance for U.S. Appl. No. 16/695,049, dated Apr. 28, 2022.
Notice of Allowance for U.S. Appl. No. 16/695,049, dated Jan. 5, 2022.
Notice of Allowance for U.S. Appl. No. 16/879,704, dated Apr. 26, 2022.
Notice of Allowance for U.S. Appl. No. 17/318,890, dated Jun. 8, 2022.
Notice of Allowance for U.S. Appl. No. 17/318,890, dated Mar. 3, 2022.
Notice of Allowance for U.S. Appl. No. 17/867,508, dated Nov. 14, 2022.
Office Action for U.S. Appl. No. 16/569,554, dated Mar. 14, 2022.
Office Action for U.S. Appl. No. 16/695,053 dated Jan. 5, 2022.
Office Action for U.S. Appl. No. 16/695,053, dated May 11, 2022.
Office Action for U.S. Appl. No. 16/695,053, dated Oct. 14, 2022.
Office Action for U.S. Appl. No. 16/804,960, dated Dec. 13, 2021.
Office Action for U.S. Appl. No. 17/134,110, dated Dec. 22, 2022.
Office Action for U.S. Appl. No. 17/134,110, dated Jun. 24, 2022.
Office Action for U.S. Appl. No. 17/859,722, dated Oct. 26, 2022.
Related Publications (1)
Number Date Country
20220060555 A1 Feb 2022 US
Continuations (3)
Number Date Country
Parent 16460798 Jul 2019 US
Child 17484004 US
Parent 15878966 Jan 2018 US
Child 16460798 US
Parent 14977810 Dec 2015 US
Child 15878966 US