Virtualization allows the abstraction and pooling of hardware resources to support virtual machines in a Software-Defined Networking (SDN) environment, such as a Software-Defined Data Center (SDDC). For example, through server virtualization, virtual machines (VMs) running different operating systems may be supported by the same physical machine (e.g., referred to as a “host”). Each VM is generally provisioned with virtual resources to run an operating system and applications. Further, through SDN, benefits similar to server virtualization may be derived for networking services. For example, logical overlay networks may be provisioned, changed, stored, deleted and restored programmatically without having to reconfigure the underlying physical hardware architecture. In practice, network device(s) may be deployed in the SDN environment to perform packet processing to facilitate communication among endpoints, such as VMs, etc. However, existing approaches for processing encapsulated encrypted packets may lack efficiency.
In the following detailed description, reference is made to the accompanying drawings, which form a part hereof. In the drawings, similar symbols typically identify similar components, unless context dictates otherwise. The illustrative embodiments described in the detailed description, drawings, and claims are not meant to be limiting. Other embodiments may be utilized, and other changes may be made, without departing from the spirit or scope of the subject matter presented here. It will be readily understood that the aspects of the present disclosure, as generally described herein, and illustrated in the drawings, can be arranged, substituted, combined, and designed in a wide variety of different configurations, all of which are explicitly contemplated herein.
Challenges relating to packet processing will now be explained using
Referring first to
Referring also to
Hypervisor 112A/112B/112C maintains a mapping between underlying hardware 111A/111B/111C and virtual resources allocated to the VMs. Hardware 111A/111B/111C includes various physical components, such as central processing unit(s) or processor(s) 120A/120B/120C; memory 122A/122B/122C; physical network interface controllers (NICs) 124A/124B/124C; and storage disk(s) 128A/128B/128C accessible via storage controller(s) 126A/126B/126C, etc. Virtual resources are allocated to each virtual machine to support a guest operating system (OS) and applications, such as virtual central processing unit (CPU), guest physical memory, virtual disk(s) and virtual network interface controller (VNIC). Hypervisor 112A/112B/112C further implements virtual switch 114A/114B/114C and logical distributed router (DR) instance 116A/116B/116C to handle egress packets from, and ingress packets to, respective VMs.
In practice, logical switches and logical distributed routers may be implemented in a distributed manner and can span multiple hosts 110A-C to connect the VMs. For example, a logical switch may be configured to provide logical layer-2 connectivity to VMs supported by different hosts. The logical switch may be implemented collectively by virtual switches 114A-C of respective hosts 110A-C and represented internally using forwarding tables (e.g., 115A-C) at the respective virtual switches 114A-C. Further, logical distributed routers that provide logical layer-3 connectivity may be implemented collectively by distributed router (DR) instances (e.g., 116A-C) of respective hosts 110A-C and represented internally using routing tables (e.g., 117A-C) at the respective DR instances. Routing tables 117A-C may be each include entries that collectively implement the respective logical distributed routers.
The VMs (e.g., VMs 131-134, 150 and 160) may send and receive packets via respective logical ports 141-146. As used herein, the term “logical port” may refer generally to a port on a logical switch to which a virtualized computing instance is connected. A “logical switch” may refer generally to an SDN construct that is collectively implemented by virtual switches of hosts 110A-C, whereas a “virtual switch” (e.g., 114A-C) may refer generally to a software switch or software implementation of a physical switch. In practice, there is usually a one-to-one mapping between a logical port on a logical switch and a virtual port on a virtual switch. However, the mapping may change in some scenarios, such as when the logical port is mapped to a different virtual port on a different virtual switch after migration of the corresponding virtualized computing instance (e.g., when the source and destination hosts do not have a distributed virtual switch spanning them).
Although examples of the present disclosure refer to virtual machines, it should be understood that a “virtual machine” running on a host is merely one example of a “virtualized computing instance” or “workload.” A virtualized computing instance may represent an addressable data compute node or isolated user space instance. In practice, any suitable technology may be used to provide isolated user space instances, not just hardware virtualization. Other virtualized computing instances may include containers (e.g., running within a VM or on top of a host operating system without the need for a hypervisor or separate operating system or implemented as an operating system level virtualization), virtual private servers, client computers, etc. Such container technology is available from, among others, Docker, Inc. The virtual machines may also be complete computational environments, containing virtual equivalents of the hardware and software components of a physical computing system.
As used herein, the term “hypervisor” may refer generally to a software layer or component that supports the execution of multiple virtualized computing instances, including system-level software in guest virtual machines that supports namespace containers such as Docker, etc. Hypervisors 114A-C may each implement any suitable virtualization technology, such as VMware ESX® or ESXi™ (available from VMware, Inc.), Kernel-based Virtual Machine (KVM), etc. The term “packet” may refer generally to a group of bits that can be transported together from a source to a destination, such as message, segment, datagram, etc. The term “traffic” may refer generally to a flow of packets. The term “layer 2” may refer generally to a Media Access Control (MAC) layer; “layer 3” to a network or Internet Protocol (IP) layer; and “layer-4” to a transport layer (e.g., using transmission control protocol (TCP) or user datagram protocol (UDP)) in the Open System Interconnection (OSI) model, although the concepts described herein may be used with other networking models.
Depending on the desired implementation, tunnel 180 may be established between a first tunnel endpoint at EDGE1150 and a second tunnel endpoint at EDGE2160. In practice, the second tunnel endpoint may be any other endpoint or non-edge router, not just EDGE2160. Tunnel 180 may be established using any suitable tunneling protocol. For example, a Virtual Private Network (VPN) based on Internet Protocol Security (IPSec) may bridge traffic in a hybrid cloud environment between first site 201 (e.g., on-prem data center) and second site 202 (e.g., public cloud environment). In practice, IPSec is a secure network protocol suite that provides data authentication, integrity and confidentiality between a pair of entities (e.g., data centers, gateways) across an IP-based network. One example in the IPSec protocol suite is Encapsulating Security Payload (ESP), which provides origin authenticity using source authentication, data integrity and confidentiality through encryption protection for IP packets. Although various examples will be discussed using IPSec-based VPN, it should be understood that any alternative and/or additional protocol(s) may be used.
In the example in
Receive-Side Processing for Encapsulated Encrypted Packets
According to examples of the present disclosure, receive-side processing for encapsulated encrypted packets may be performed in a more efficient manner. In particular, post-cryptography processing may be distributed among multiple processing units of EDGE1150 based on (cleartext) content of decrypted packets. This way, receive-side scaling (RSS) may be implemented for post-cryptography operations to improve parallelism at EDGE1150 and therefore performance. Examples of the present disclosure should be contrasted against conventional approaches that assign packets to different processing units based on their non-encrypted outer header because the rest of the packets are encrypted.
Examples of the present disclosure may be performed by any suitable “computer system” configured to perform receive-side processing. In the following, an example “computer system” will be explained using EDGE1150, which is deployed at the edge of first site 201 to facilitate communication among VMs 131-135. Depending on the desired implementation, EDGE1150 may implement any suitable data-plane packet processing engine(s) to perform packet processing. One example is the Data Plane Development Kit (DPDK), which is an open-source Linux Foundation project that provides a set of data plane libraries and (physical or virtual) NIC drivers to accelerate fast packet processing.
As used herein, the term “receive-side processing” may be used generally to include various operations performed by a computer system in response to receiving ingress or incoming encapsulated encrypted packets, including authentication, decryption, decapsulation, encapsulation, firewall, load balancing, forwarding to destination, etc. To support RSS, EDGE 150 may include multiple processing units labelled as core1, . . . , coreN (see 151 in
In more detail,
At 310 and 320 in
At 340 and 350 in
As will be described using
As will be described using
Example Architecture
Packet processing by EDGE1150 will be explained using 401-408. At 401-402, in response to detecting a packet at first NIC1421, the packet may be directed to DPDK application 440 in user space 411. At 407, after performing any necessary packet processing, DPDK application 440 may direct the processed packet towards second NIC2422. Alternatively, at 403, DPDK application 440 may direct the packet to network interface 450 via TX queue 451. In this case, at 404-405, KNI packet process 460 may retrieve (e.g., poll) packet(s) from TX queue 451 before performing any necessary packet processing and pushing the packet(s) into RX queue 452 of network interface 450. At 406, DPDK application 440 may retrieve (e.g., poll) packet(s) from RX queue for any necessary packet processing. At 407-408, DPDK application 440 may perform necessary processing and forward packet(s) to second NIC2422. It should be noted that a particular ingress packet (e.g., “PACKET IN” 401) is not necessarily the same as an egress packet (e.g., “PACKET OUT” 408) in
To support post-cryptography RSS, a receive-side datapath on DPDK application 440 may include various components 441-444 (to be explained below) and multiple processing units 445 denoted as core1, . . . , coreN (see also 151 in
IPSec-Based VPN Example
Examples of the present disclosure will now be explained using IPSec-based VPN. It should be understood that any alternative and/or additional protocol(s) may be used. In more detail,
(a) Tunnel Establishment
At 510 in
Using IPSec for example, an SA may be uniquely identifiable using a security parameter index (SPI), source and destination address information, and a security protocol such as Encapsulating Security Payload (ESP) or Authentication Header (AH). Based on the SA, EDGE2160 may perform encryption and encapsulation for egress packets originating from source endpoints (e.g., VM4134 and VM5135) before forwarding encapsulated encrypted packets over tunnel 180. At the receive-side, the reverse is performed. Based on the SA, EDGE1150 to perform decryption and decapsulation before forwarding decrypted packets towards destination endpoints (e.g., VM1131 and VM2132). To identify the SA, an associated SPI may be added an identification tag to an outer header of each encapsulated encrypted packet travelling over tunnel 180. Although not shown for simplicity, each encapsulated encrypted packet may be padded with encryption-related data, such as ESP trailer data and ESP authentication data before being sent over tunnel 180.
(b) Decryption and Decapsulation
At 520 and 530 in
ENCAP1611, ENCAP2612 and ENCAP3613 include respective encrypted inner packets denoted as (I1*, P1*), (I2*, P2*) and (I3*, P3*). For ENCAP1611 and ENCAP3613, their respective encrypted inner packets (I1*, P1*) and (I3*, P3*) are addressed from source address=IP-VM4 associated with VM4134 to destination address=IP-VM1 associated with VM1131. For ENCAP2612, encrypted inner packets (I2*, P2*) is addressed from source address=IP-VM5 associated with VM5135 to destination address=IP-VM2 associated with VM2132. In other words, ENCAP1611, ENCAP2612 and ENCAP3613 belong to the same SA (i.e., SPI=X), but two different packet flows. See 602, 604 in
At 540, 550 and 560, DPDK application 440 may perform receive-side processing for ENCAP1611, ENCAP2612 and ENCAP3613, such as decapsulation, decryption, authentication, etc. Using IPSec as an example, DPDK application 440 may implement various modules for processing ENCAP1611, ENCAP2612 and ENCAP3613, including “ipsec_input” 441, “crypto_pmd” 442 and “ipsec_input_finish” 443. In this case, block 540 may involve “ipsec_input” 441 parsing the outer header (O1, O2, O3) of ENCAP1611, ENCAP2612 and ENCAP3613 to identify an SA associated with SPI=X, and preparing any necessary transactions for a cryptography driver.
At 550 in
At 560 in
(c) Post-Cryptography RSS
At 570 in
Using a tuple-based approach, block 570 may involve parsing the decrypted inner header to extract tuple information and calculating a hash value associated with the tuple information to select core1 based on the hash value. One example is to map the tuple information to a particular processing core by applying an exclusive OR (XOR) operation as follows:
i=XOR of tuple modulo N.
Here, i∈{1, . . . , N} represents an index of the selected processing unit, N represents the total number of available processing units and tuple represents 5-tuple=(source IP address, destination IP address, source port number, destination port number, protocol) extractable from the decrypted inner header (I1, I2, I3).
In the example in
In another example, a load-based approach may be implemented to select core_i based on a load level associated with each corei. For example, if the load level exceeds a predetermined threshold, a different corei may be selected. Subsequent packets from the same TCP flow may be assigned to the same corei. Depending on the desired implementation, the hash value may be calculated based on additional or alternative tuple information from inner packets 631-633.
At 580 and 590 in
Using examples of the present disclosure, RSS may be performed for packets belonging to the same SA, but different packet flows. This allows scaling of IPSec performance for a particular SA supported by tunnel 180, such as in the case of route-based VPN. This should be contrasted against conventional approaches that are able to distribute traffic based on non-encrypted content of the outer header, such as the SPI. By performing load balancing or core selection based on decrypted content of packets, post-cryptography processing may be distributed more efficiently among multiple processing units 445 at EDGE1150. Although explained using EDGE1150, it should be understood that EDGE2160 may implement the examples of the present disclosure to perform receive-side processing for encapsulated encrypted packets on the reverse path, such as from VM1131 to VM4134 or from VM2132 to VM5135.
Examples of the present disclosure may be implemented for tunnel 180 supporting multiple SAs. Using an example with two SAs, different packet flows belonging to a first SA may be distributed among a first subset of processing units 445, such as core1, . . . , coreM, where M<N. Additionally, different packet flows belong to a second SA may be distributed among a second subset of processing units 445, such as core(M+1), . . . , coreN. The size of the first subset (M) may be configured according to a priority level associated with the first SA. For example, more processing units may be allocated to the first SA to assign a higher priority level compared to the second SA.
Container Implementation
Although discussed using VMs 131-135, it should be understood that receive-side processing for encapsulated encrypted packets may be performed for other virtualized computing instances, such as containers, etc. The term “container” (also known as “container instance”) is used generally to describe an application that is encapsulated with all its dependencies (e.g., binaries, libraries, etc.). For example, multiple containers may be executed as isolated processes inside VM1131, where a different VNIC is configured for each container. Each container is “OS-less”, meaning that it does not include any OS that could weigh 10s of Gigabytes (GB). This makes containers more lightweight, portable, efficient and suitable for delivery into an isolated OS environment. Running containers inside a VM (known as “containers-on-virtual-machine” approach) not only leverages the benefits of container technologies but also that of virtualization technologies. Using the examples in
Computer System
The above examples can be implemented by hardware (including hardware logic circuitry), software or firmware or a combination thereof. The above examples may be implemented by any suitable computing device, computer system, etc. The computer system may include processor(s), memory unit(s) and physical NIC(s) that may communicate with each other via a communication bus, etc. The computer system may include a non-transitory computer-readable medium having stored thereon instructions or program code that, when executed by the processor, cause the processor to perform processes described herein with reference to
The techniques introduced above can be implemented in special-purpose hardwired circuitry, in software and/or firmware in conjunction with programmable circuitry, or in a combination thereof. Special-purpose hardwired circuitry may be in the form of, for example, one or more application-specific integrated circuits (ASICs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), and others. The term ‘processor’ is to be interpreted broadly to include a processing unit, ASIC, logic unit, or programmable gate array etc.
The foregoing detailed description has set forth various embodiments of the devices and/or processes via the use of block diagrams, flowcharts, and/or examples. Insofar as such block diagrams, flowcharts, and/or examples contain one or more functions and/or operations, it will be understood by those within the art that each function and/or operation within such block diagrams, flowcharts, or examples can be implemented, individually and/or collectively, by a wide range of hardware, software, firmware, or any combination thereof.
Those skilled in the art will recognize that some aspects of the embodiments disclosed herein, in whole or in part, can be equivalently implemented in integrated circuits, as one or more computer programs running on one or more computers (e.g., as one or more programs running on one or more computing systems), as one or more programs running on one or more processors (e.g., as one or more programs running on one or more microprocessors), as firmware, or as virtually any combination thereof, and that designing the circuitry and/or writing the code for the software and or firmware would be well within the skill of one of skill in the art in light of this disclosure.
Software and/or other instructions to implement the techniques introduced here may be stored on a non-transitory computer-readable storage medium and may be executed by one or more general-purpose or special-purpose programmable microprocessors. A “computer-readable storage medium”, as the term is used herein, includes any mechanism that provides (i.e., stores and/or transmits) information in a form accessible by a machine (e.g., a computer, network device, personal digital assistant (PDA), mobile device, manufacturing tool, any device with a set of one or more processors, etc.). A computer-readable storage medium may include recordable/non recordable media (e.g., read-only memory (ROM), random access memory (RAM), magnetic disk or optical storage media, flash memory devices, etc.).
The drawings are only illustrations of an example, wherein the units or procedure shown in the drawings are not necessarily essential for implementing the present disclosure. Those skilled in the art will understand that the units in the device in the examples can be arranged in the device in the examples as described or can be alternatively located in one or more devices different from that in the examples. The units in the examples described can be combined into one module or further divided into a plurality of sub-units.
Number | Name | Date | Kind |
---|---|---|---|
6901452 | Bertagna | May 2005 | B1 |
6968441 | Schnee | Nov 2005 | B1 |
7003118 | Yang et al. | Feb 2006 | B1 |
7181612 | Pellacuru et al. | Feb 2007 | B1 |
7243225 | Poeluev et al. | Jul 2007 | B2 |
7555544 | Rattner et al. | Jun 2009 | B1 |
7814310 | Bouchard et al. | Oct 2010 | B2 |
7962358 | Fernandez et al. | Jun 2011 | B1 |
8175078 | Voit et al. | May 2012 | B2 |
8356346 | Datta et al. | Jan 2013 | B2 |
8547837 | Ronciak et al. | Oct 2013 | B2 |
9483286 | Basavaiah et al. | Nov 2016 | B2 |
9535750 | Wilkes et al. | Jan 2017 | B1 |
9588813 | Dubey et al. | Mar 2017 | B1 |
9674088 | Sivaramakrishnan et al. | Jun 2017 | B1 |
9712460 | Friend | Jul 2017 | B1 |
9755972 | Mao et al. | Sep 2017 | B1 |
9929970 | Matthews et al. | Mar 2018 | B1 |
10020984 | Jork et al. | Jul 2018 | B1 |
10257167 | Matthews et al. | Apr 2019 | B1 |
10498529 | Hashmi | Dec 2019 | B1 |
10498708 | Wang et al. | Dec 2019 | B2 |
10623372 | Wang et al. | Apr 2020 | B2 |
10701107 | Gopal et al. | Jun 2020 | B2 |
20020097724 | Halme et al. | Jul 2002 | A1 |
20030088787 | Egevang | May 2003 | A1 |
20040143734 | Buer et al. | Jul 2004 | A1 |
20040225895 | Mukherjee et al. | Nov 2004 | A1 |
20050198531 | Kaniz et al. | Sep 2005 | A1 |
20050213603 | Karighattam et al. | Sep 2005 | A1 |
20060002388 | Grebus et al. | Jan 2006 | A1 |
20070130352 | Chhabra et al. | Jun 2007 | A1 |
20080123593 | Fujita et al. | May 2008 | A1 |
20080144625 | Wu et al. | Jun 2008 | A1 |
20080165964 | Lewis et al. | Jul 2008 | A1 |
20080307024 | Michaels et al. | Dec 2008 | A1 |
20090199290 | McCullough et al. | Aug 2009 | A1 |
20090287848 | Kamura et al. | Nov 2009 | A1 |
20100153715 | Kauppinen et al. | Jun 2010 | A1 |
20100191958 | Chen | Jul 2010 | A1 |
20100217949 | Schopp et al. | Aug 2010 | A1 |
20110113236 | Chenard et al. | May 2011 | A1 |
20110153985 | Saha et al. | Jun 2011 | A1 |
20120027314 | Lee et al. | Feb 2012 | A1 |
20120102278 | Joffray et al. | Apr 2012 | A1 |
20120124591 | Cadambi et al. | May 2012 | A1 |
20120170459 | Olesinski et al. | Jul 2012 | A1 |
20120254353 | Baba et al. | Oct 2012 | A1 |
20130201989 | Hu et al. | Aug 2013 | A1 |
20140089480 | Zhu | Mar 2014 | A1 |
20140108665 | Arora et al. | Apr 2014 | A1 |
20140313932 | Saltsidis | Oct 2014 | A1 |
20150195138 | Horman | Jul 2015 | A1 |
20150263974 | Jain et al. | Sep 2015 | A1 |
20160057108 | Hu | Feb 2016 | A1 |
20160085571 | Kim et al. | Mar 2016 | A1 |
20160087888 | Jain et al. | Mar 2016 | A1 |
20160088072 | Likhtarov et al. | Mar 2016 | A1 |
20160092259 | Mehta et al. | Mar 2016 | A1 |
20160142307 | Kamper et al. | May 2016 | A1 |
20160182509 | Kantecki | Jun 2016 | A1 |
20160212098 | Roch | Jul 2016 | A1 |
20160226815 | Wan et al. | Aug 2016 | A1 |
20160277478 | Narasimhamurthy | Sep 2016 | A1 |
20160352628 | Reddy et al. | Dec 2016 | A1 |
20170005931 | Mehta et al. | Jan 2017 | A1 |
20170024293 | Bell et al. | Jan 2017 | A1 |
20170054603 | Kulkarni et al. | Feb 2017 | A1 |
20170063808 | Manapragada et al. | Mar 2017 | A1 |
20170063979 | Saeki | Mar 2017 | A1 |
20170163598 | Shen et al. | Jun 2017 | A1 |
20170374025 | Pan | Dec 2017 | A1 |
20180054458 | Marck et al. | Feb 2018 | A1 |
20180062875 | Tumuluru | Mar 2018 | A1 |
20180067786 | Nguyen Trung et al. | Mar 2018 | A1 |
20180069924 | Tumuluru et al. | Mar 2018 | A1 |
20180123950 | Garg et al. | May 2018 | A1 |
20180131521 | Yang et al. | May 2018 | A1 |
20180191642 | Biederman et al. | Jul 2018 | A1 |
20180241655 | Tsirkin | Aug 2018 | A1 |
20180343146 | Dunbar et al. | Nov 2018 | A1 |
20190114206 | Murugesan et al. | Apr 2019 | A1 |
20190140984 | Agarwal et al. | May 2019 | A1 |
20190173841 | Wang et al. | Jun 2019 | A1 |
20190173850 | Jain et al. | Jun 2019 | A1 |
20190173851 | Jain | Jun 2019 | A1 |
20190173920 | Gopal et al. | Jun 2019 | A1 |
20190190892 | Menachem et al. | Jun 2019 | A1 |
20190266217 | Arakawa et al. | Aug 2019 | A1 |
20190342266 | Ramachandran et al. | Nov 2019 | A1 |
20200084192 | Wang et al. | Mar 2020 | A1 |
20200099670 | Kessler | Mar 2020 | A1 |
20200120078 | Mao et al. | Apr 2020 | A1 |
20200351254 | Xiong et al. | Nov 2020 | A1 |
20200403922 | Yu et al. | Dec 2020 | A1 |
20210020727 | Lin et al. | Jan 2021 | A1 |
20210021523 | Wang et al. | Jan 2021 | A1 |
20210400029 | Wang et al. | Dec 2021 | A1 |
Number | Date | Country |
---|---|---|
102801695 | Nov 2012 | CN |
108540559 | Sep 2018 | CN |
110677426 | Jan 2020 | CN |
20030013496 | Feb 2003 | KR |
2016020727 | Feb 2016 | WO |
Entry |
---|
Joongi Kim et al., “NBA (Network Balancing Act): A High-performance Packet Processing Framework for Heterogeneous Processors”, KAIST, Apr. 21-25, 2015, pp. 14. |
Jeongseok Son et al., “Protego: Cloud-Scale Multitenant IPsec Gateway”, in the proceedings of the 2017 USENIX annual technical conference, Jul. 12-14, 2017, pp. 15. |
C. Hopps, Analysis of an Equal-Cost Multi-Path Algorithm:, Network Working Group—NextHop Technologies, Nov. 2000. |
Cisco IOS XE Release 3S, “ECMP Load Balancing”, MPLS: Layer 3 VPNs Configuration Guide, Cisco IOS XE Release 3S (Cisco ASR 900 Series), May 2018. |
Jesper Dangaard Brouer, “[Net-next, V8, 0/5] New Bpf Cpumap Type for XDP_Redirect”, Oct. 16, 2017, Retreived from Internet at <URL: http://patchwork.ozlabs.org/cover/826223/>. |
Jeongseok Son et al., “Protego: Cloud-Scale Multitenant IPsec Gateway”, The Proceedings of the 2017 USENIX Annual technical Conference, Jul. 12-14, 2017. |
Shin Muramatsu et al., “VSE: Virtual Switch Extension for Adaptive CPU Core Assignment in Softirq”, 2014 IEEE 6th International Conference on Cloud Computing Technology and Science, 2014, pp. 923-928. |
Ying Ye et al., “MARACAS: A Real-Time Multicore VCPU Scheduling Framework”, 2016 IEEE Real-Time Systems Symposium, 2016, pp. 179-190. |
Antoine Kaufmann et al., “High Performance Packet Processing with FlexNIC”, ASPLOS'16, Apr. 2-6, 2016. |
“Receive Side Scaling (RSS) Guide by Microsoft”, Apr. 19, 2017, <URL: https://docs.microsoft.com/en-us/windows-hardware/drivers/network/ndis-receive-side-scaling2>, pp. 3936. |
Non-Published Commonly Owned U.S. Appl. No. 16/016,360, filed Jun. 22, 2018 , 36 pages, VMware, Inc. |
Non-Published Commonly Owned U.S. Appl. No. 16/802,580, filed Feb. 27, 2020 , 31 pages, VMware, Inc. |
Non-Published Commonly Owned U.S. Appl. No. 16/893,450, filed Jun. 5, 2020, 36 pages, VMware, Inc. |
Non-Published Commonly Owned U.S. Appl. No. 17/016,596, filed Sep. 10, 2020, 36 pages, VMware, Inc. |
Craig A. Shue et al., “IPSec: Performance Analysis and Enhancements”, IEEE Communications Society subject matter experts for publication in the ICC 2007 proceedings, 2007, pp. 1527-1532. |
Tudor Marian et al., “NetSlices: Scalable Multi-Core Packet Processing in User-Space”, ANCS'12, Oct. 29-30, 2012. |
Non-Published Commonly Owned U.S. Appl. No. 16/906,905, filed Jun. 19, 2020, 126 pages, VMware, Inc. |
Ying Ye et al., “MARACAS: A Real-Time Multicore VCPU Scheduling Framework”, 2016 IEEE Real=Time Systems Symposium (RTSS), Porto, 2016, 12 pages. |
“VPN Setup Tutorial Guide”, Internet-Computer-Security.com, Retrieved on May 1, 2019 at <URL: http://www.internet-computer-security.com/VPN-Guide/VPN-Tutorial-G>, 7 pages. |
“IPsec VPN Overview”, info_outline Platform and Release Support, Mar. 31, 2019, 25 pages. |
International Search Report and Written Opinion of the International Searching Authority, International application No. PCT/US2022/011726, dated Apr. 8, 2022. |
Elaine Barker et al., “Guide to IPsec VPNs”, National Institute of Standards and Technology (NIST), Jun. 30, 2020, 166 pages, Retrieved from the Internte: URL <https://nvlpubs.nist.gov/nistpubs/Specia1Publications/NIST.SP.800-77rl.pdf/, Retrieved on Jun. 30, 2022, Section 3. |
Number | Date | Country | |
---|---|---|---|
20210185025 A1 | Jun 2021 | US |