research-article

GEN: A GPU-Accelerated Elastic Framework for NFV

Authors:

Jianping WuAuthors Info & Claims

APNet '18: Proceedings of the 2nd Asia-Pacific Workshop on Networking

Pages 57 - 64

https://doi.org/10.1145/3232565.3234510

Published: 01 August 2018 Publication History

Abstract

Network Function Virtualization (NFV) has the potential to enhance service delivery flexibility and reduce overall costs by provisioning software-based service function chains (SFCs) on commodity hardware. However, we observe that existing CPU-based SFC solutions cannot achieve both high performance and high elasticity simultaneously. To address such a critical challenge, we seek beyond CPU and exploit the capability of Graphics Processing Unit (GPU) to support NFV. We propose GEN, a GPU-based high performance and elastic framework for NFV. As opposed to pipeline-based SFCs in existing GPU-based NFV systems, GEN proposes to support RTC-based SFCs to improve processing performance. Meanwhile, GEN offers great elasticity of network function (NF) scaling up and down by allocating a different number of fine-grained GPU threads to an NF during runtime. We have implemented a prototype of GEN. Preliminary evaluation results demonstrate that GEN improves performance with RTC-based SFCs, and supports adaptive, precise, and fast NF scaling for NFV.

References

[1]

Theophilus Benson, Aditya Akella, and David A Maltz. 2010. Network traffic characteristics of data centers in the wild. In Proceedings of the 10th ACM SIGCOMM conference on Internet measurement. ACM, 267--280.

Digital Library

[2]

Anat Bremler-Barr, Yotam Harchol, and David Hay. 2016. OpenBox: a software-defined framework for developing, deploying, and managing network functions. In Proceedings of the 2016 conference on ACM SIGCOMM 2016 Conference. ACM, 511--524.

Digital Library

[3]

Rohan Gandhi, Hongqiang Harry Liu, Y Charlie Hu, Guohan Lu, Jitendra Padhye, Lihua Yuan, and Ming Zhang. 2015. Duet: Cloud scale load balancing with hardware and software. ACM SIGCOMM Computer Communication Review 44, 4 (2015), 27--38.

Digital Library

[4]

Aaron Gember, Anand Krishnamurthy, Saul St John, Robert Grandl, Xiaoyang Gao, Ashok Anand, Theophilus Benson, Vyas Sekar, and Aditya Akella. 2013. Stratos: A Network-Aware Orchestration Layer for Virtual Middleboxes in Clouds. arXiv preprint arXiv:1305.0209 (2013).

[5]

Aaron Gember-Jacobson, Raajay Viswanathan, Chaithan Prakash, Robert Grandl, Junaid Khalid, Sourav Das, and Aditya Akella. 2014. OpenNF: Enabling innovation in network function control. In ACM SIGCOMM Computer Communication Review, Vol. 44. 163--174.

Digital Library

[6]

Younghwan Go, Muhammad Asim Jamshed, Young-Gyoun Moon, Changho Hwang, and KyoungSoo Park. 2017. APUNet: Revitalizing GPU as Packet Processing Accelerator. In USENIX Symposium on Networked Systems Design and Implementation. 83--96.

Digital Library

[7]

R Guerzoni et al. 2012. Network functions virtualisation: an introduction, benefits, enablers, challenges and call for action, introductory white paper. In SDN and OpenFlow World Congress.

[8]

Kshitij Gupta, Jeff A Stuart, and John D Owens. 2012. A study of persistent threads style GPU programming for GPGPU workloads. In Innovative Parallel Computing (InPar), 2012. IEEE, 1--14.

[9]

W Haeffner, J Napper, M Stiemerling, D Lopez, and J Uttaro. 2014. Service function chaining use cases in mobile networks. draft-ietf-sfc-use-case-mobility-01 (2014).

[10]

J Halpern and C Pignataro. 2015. Service Function Chaining (SFC) Architecture. draft-ietf-sfc-architecture-07 (work in progress) (2015).

[11]

Sangjin Han, Keon Jang, KyoungSoo Park, and Sue Moon. 2010. PacketShader: a GPU-accelerated software router. In ACM SIGCOMM Computer Communication Review, Vol. 40. ACM, 195--206.

Digital Library

[12]

Intel. 2018. Data Plane Development Kit (DPDK). http://dpdk.org

[13]

Muhammad Asim Jamshed, Jihyung Lee, Sangwoo Moon, Insu Yun, Deokjin Kim, Sungryoul Lee, Yung Yi, and KyoungSoo Park. 2012. Kargus: a highly-scalable software-based intrusion detection system. In Proceedings of the 2012 ACM conference on Computer and communications security. ACM, 317--328.

Digital Library

[14]

Dilip A Joseph, Arsalan Tavakoli, and Ion Stoica. 2008. A policy-aware switching layer for data centers. In ACM SIGCOMM Computer Communication Review, Vol. 38. ACM, 51--62.

Digital Library

[15]

Anuj Kalia, Dong Zhou, Michael Kaminsky, and David G Andersen. 2015. Raising the Bar for Using GPUs in Software Packet Processing. In USENIX Symposium on Networked Systems Design and Implementation. 409--423.

Digital Library

[16]

Georgios P Katsikas, Tom Barbette, Dejan Kostic, Rebecca Steinert, and Gerald Q Maguire Jr. 2018. Metron: NFV Service Chains at the True Speed of the Underlying Hardware. In USENIX Symposium on Networked Systems Design and Implementation. USENIX Association.

[17]

Joongi Kim, Keon Jang, Keunhong Lee, Sangwook Ma, Junhyun Shim, and Sue Moon. 2015. NBA (network balancing act): A high-performance packet processing framework for heterogeneous processors. In Proceedings of the Tenth European Conference on Computer Systems. ACM, 22.

Digital Library

[18]

S Kumar, M Tufail, S Majee, C Captari, and S Homma. 2015. Service Function Chaining Use Cases in Data Centers. IETF SFC WG (2015).

[19]

CUDA Nvidia. 2010. Programming guide.

[20]

Shoumik Palkar, Chang Lan, Sangjin Han, Keon Jang, Aurojit Panda, Sylvia Ratnasamy, Luigi Rizzo, and Scott Shenker. 2015. E2: a framework for NFV applications. In Proceedings of the 25th Symposium on Operating Systems Principles. ACM, 121--136.

Digital Library

[21]

Aurojit Panda, Sangjin Han, Keon Jang, Melvin Walls, Sylvia Ratnasamy, and Scott Shenker. {n. d.}. NetBricks: Taking the V out of NFV. In 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16), USENIX OSDI, Vol. 16.

Digital Library

[22]

Zafar Ayyub Qazi, Cheng-Chun Tu, Luis Chiang, Rui Miao, Vyas Sekar, and Minlan Yu. 2013. SIMPLE-fying middlebox policy enforcement using SDN. In ACM SIGCOMM computer communication review, Vol. 43. ACM, 27--38.

Digital Library

[23]

P Quinn and T Nadeau. 2014. Service function chaining problem statement. draft-ietf-sfc-problem-statement-10 (work in progress) (2014).

[24]

Shriram Rajagopalan, Dan Williams, Hani Jamjoom, and Andrew Warfield. 2013. Split/merge: System support for elastic execution in virtual middleboxes. In Presented as part of the 10th USENIX Symposium on Networked Systems Design and Implementation. 227--240.

Digital Library

[25]

Vyas Sekar, Norbert Egi, Sylvia Ratnasamy, Michael K Reiter, and Guangyu Shi. 2012. Design and implementation of a consolidated middlebox architecture. In Proceedings of the 9th USENIX conference on Networked Systems Design and Implementation. USENIX Association, 24--24.

Digital Library

[26]

Justine Sherry, Shaddi Hasan, Colin Scott, Arvind Krishnamurthy, Sylvia Ratnasamy, and Vyas Sekar. 2012. Making middleboxes someone else's problem: network processing as a cloud service. ACM SIGCOMM Computer Communication Review 42, 4 (2012), 13--24.

Digital Library

[27]

Snort. 2018. Snort community rules. https://www.snort.org/

[28]

Chen Sun, Jun Bi, Zhilong Zheng, Heng Yu, and Hongxin Hu. 2017. NFP: Enabling Network Function Parallelism in NFV. In Proceedings of the Conference of the ACM Special Interest Group on Data Communication. ACM, 43--56.

Digital Library

[29]

Weibin Sun and Robert Ricci. 2013. Fast and flexible: parallel packet processing with GPUs and click. In Architectures for Networking and Communications Systems, ACM/IEEE Symposium on. IEEE, 25--35.

Digital Library

[30]

Giorgos Vasiliadis, Lazaros Koromilas, Michalis Polychronakis, and Sotiris Ioannidis. 2014. GASPP: A GPU-Accelerated Stateful Packet Processing Framework. In USENIX Annual Technical Conference. 321--332.

Digital Library

[31]

Tsung Tai Yeh, Amit Sabne, Putt Sakdhnagool, Rudolf Eigenmann, and Timothy G Rogers. 2017. Pagoda: Fine-Grained GPU Resource Virtualization for Narrow Tasks. In Proceedings of the 22nd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. ACM, 221--234.

Digital Library

[32]

Xiaodong Yi, Jingpu Duan, and Chuan Wu. 2017. GPUNFV: a GPU-Accelerated NFV System. In Proceedings of the First Asia-Pacific Workshop on Networking. ACM, 85--91.

Digital Library

[33]

Kai Zhang, Bingsheng He, Jiayu Hu, Zeke Wang, Bei Hua, Jiayi Meng, and Lishan Yang. 2018. G-NET: Effective GPU Sharing in NFV Systems. In USENIX Symposium on Networked Systems Design and Implementation. USENIX, 187--200.

[34]

Yang Zhang, Bilal Anwer, Vijay Gopalakrishnan, Bo Han, Joshua Reich, Aman Shaikh, and Zhi-Li Zhang. 2017. ParaBox: Exploiting Parallelism for Virtual Network Functions in Service Chaining. In Proceedings of the Symposium on SDN Research. ACM, 143--149.

Digital Library

Cited By

Zeng DZhu AGu LChen QGuo M(2023)On Efficient Packet Batching and Resource Allocation for GPU based NFV Acceleration2023 IEEE/ACM 31st International Symposium on Quality of Service (IWQoS)10.1109/IWQoS57198.2023.10188696(1-10)Online publication date: 19-Jun-2023
https://doi.org/10.1109/IWQoS57198.2023.10188696
Chaurasia AGarg ARaman BKurkure USivaraman HVu LVeeraswamy S(2022)Simmer: Rate proportional scheduling to reduce packet drops in vGPU based NF chainsProceedings of the 51st International Conference on Parallel Processing10.1145/3545008.3545068(1-11)Online publication date: 29-Aug-2022
https://dl.acm.org/doi/10.1145/3545008.3545068
Haavisto JCholez TRiekki J(2022)Unleashing GPUs for Network Function Virtualization: an open architecture based on Vulkan and KubernetesNOMS 2022-2022 IEEE/IFIP Network Operations and Management Symposium10.1109/NOMS54207.2022.9789822(1-8)Online publication date: 25-Apr-2022
https://dl.acm.org/doi/10.1109/NOMS54207.2022.9789822
Show More Cited By

Index Terms

GEN: A GPU-Accelerated Elastic Framework for NFV
1. Networks
  1. Network components
    1. Middle boxes / network appliances
  2. Network services
    1. Programmable networks

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cover image ACM Other conferences

APNet '18: Proceedings of the 2nd Asia-Pacific Workshop on Networking

August 2018

78 pages

ISBN:9781450363952

DOI:10.1145/3232565

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGCOMM: ACM Special Interest Group on Data Communication
SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing
Tsinghua University: TsingHua University Philips Research

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New York, NY, United States

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Published: 01 August 2018

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National Science Foundation of China
National Key R&D Program of China

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APNet '18

APNet '18: 2nd Asia-Pacific Workshop on Networking

August 1 - 3, 2018

Beijing, China

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Cited By

Zeng DZhu AGu LChen QGuo M(2023)On Efficient Packet Batching and Resource Allocation for GPU based NFV Acceleration2023 IEEE/ACM 31st International Symposium on Quality of Service (IWQoS)10.1109/IWQoS57198.2023.10188696(1-10)Online publication date: 19-Jun-2023
https://doi.org/10.1109/IWQoS57198.2023.10188696
Chaurasia AGarg ARaman BKurkure USivaraman HVu LVeeraswamy S(2022)Simmer: Rate proportional scheduling to reduce packet drops in vGPU based NF chainsProceedings of the 51st International Conference on Parallel Processing10.1145/3545008.3545068(1-11)Online publication date: 29-Aug-2022
https://dl.acm.org/doi/10.1145/3545008.3545068
Haavisto JCholez TRiekki J(2022)Unleashing GPUs for Network Function Virtualization: an open architecture based on Vulkan and KubernetesNOMS 2022-2022 IEEE/IFIP Network Operations and Management Symposium10.1109/NOMS54207.2022.9789822(1-8)Online publication date: 25-Apr-2022
https://dl.acm.org/doi/10.1109/NOMS54207.2022.9789822
Jung CKim SKim YYeom I(2022)Virtualizing GPU direct packet I/O on commodity Ethernet to accelerate GPU-NFVJournal of Network and Computer Applications10.1016/j.jnca.2022.103480206(103480)Online publication date: Oct-2022
https://doi.org/10.1016/j.jnca.2022.103480
Zhang TQiu HLinguaglossa LCerroni WGiaccone P(2021)NFV Platforms: Taxonomy, Design Choices and Future ChallengesIEEE Transactions on Network and Service Management10.1109/TNSM.2020.304538118:1(30-48)Online publication date: Mar-2021
https://doi.org/10.1109/TNSM.2020.3045381
Xu ZZhang ZLui JLiang WXia QZhou PXu WWu G(2020)Affinity-Aware VNF Placement in Mobile Edge Clouds via Leveraging GPUsIEEE Transactions on Computers10.1109/TC.2020.3041629(1-1)Online publication date: 2020
https://doi.org/10.1109/TC.2020.3041629
Xiang ZGabriel FUrbano ENguyen GReisslein MFitzek F(2019)Reducing Latency in Virtual Machines: Enabling Tactile Internet for Human-Machine Co-WorkingIEEE Journal on Selected Areas in Communications10.1109/JSAC.2019.290678837:5(1098-1116)Online publication date: May-2019
https://doi.org/10.1109/JSAC.2019.2906788

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