research-article

GPUNFV: a GPU-Accelerated NFV System

Authors:

Chuan WuAuthors Info & Claims

APNet '17: Proceedings of the First Asia-Pacific Workshop on Networking

Pages 85 - 91

https://doi.org/10.1145/3106989.3106990

Published: 03 August 2017 Publication History

Abstract

This paper presents GPUNFV, a high-performance NFV system providing flow-level micro services for stateful service chains with Graphics Processing Unit (GPU) acceleration. GPUNFV exploits the massively-parallel processing power of GPU to maximize the throughput of the NFV system. Combined with the customized flow handler, GPUNFV achieves a much better throughput than the existing NFV systems. With a carefully designed GPU-based virtualized network function framework, GPUNFV is able to efficiently support both stateful and stateless network functions. We have implemented a number of GPU-based network functions and a preliminary GPUNFV system to demonstrate the lexibility and potential of our design.

References

[1]

2010. Actor Modle. https://en.wikipedia.org/wiki/Actor_model. (2010).

[2]

2010. Erlang. https://www.erlang.org/. (2010).

[3]

2010. GEFORCE GTX 1080. https://www.nvidia.com/. (2010).

[4]

2010. Scala Akka. akka.io/. (2010).

[5]

2015. Intel Data Plane Development Kit. http://dpdk.org/. (2015).

[6]

Aaron Gember, Robert Grandl, Ashok Anand, Theophilus Benson, and Aditya Akella. 2012. Stratos: Virtual middleboxes as first-class entities. UW-Madison TR1771 (2012), 12.

[7]

Aaron Gember-Jacobson, Raajay Viswanathan, Chaithan Prakash, Robert Grandl, Junaid Khalid, Sourav Das, and Aditya Akella. 2015. OpenNF: Enabling innovation in network function control. ACM SIGCOMM Computer Communication Review 44, 4 (2015), 163--174.

Digital Library

[8]

Sangjin Han, Keon Jang, Aurojit Panda, Shoumik Palkar, Dongsu Han, and Sylvia Ratnasamy. 2015. SoftNIC: A software NIC to augment hardware. Dept. EECS, Univ. California, Berkeley, Berkeley, CA, USA, Tech. Rep. UCB/EECS-2015-155 (2015).

[9]

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

Digital Library

[10]

Tianyi David Han and Tarek S Abdelrahman. 2011. Reducing branch divergence in GPU programs. In Proceedings of the Fourth Workshop on General Purpose Processing on Graphics Processing Units. ACM, 3.

Digital Library

[11]

Jinho Hwang, KK Ramakrishnan, and Timothy Wood. 2015. NetVM: high performance and flexible networking using virtualization on commodity platforms. IEEE Transactions on Network and Service Management 12, 1 (2015), 34--47.

Digital Library

[12]

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

[13]

Anuj Kalia, Dong Zhou, Michael Kaminsky, and David G Andersen. 2015. Raising the Bar for Using GPUs in Software Packet Processing. In NSDI. 409--423.

Digital Library

[14]

Kang Kang and Yangdong Steve Deng. 2011. Scalable packet classification via GPU metaprogramming. In Design, Automation & Test in Europe Conference & Exhibition (DATE), 2011. IEEE, 1--4.

[15]

Eddie Kohler, Robert Morris, Benjie Chen, John Jannotti, and M Frans Kaashoek. 2000. The Click modular router. ACM Transactions on Computer Systems (TOCS) 18, 3 (2000), 263--297.

Digital Library

[16]

Joao Martins, Mohamed Ahmed, Costin Raiciu, Vladimir Olteanu, Michio Honda, Roberto Bifulco, and Felipe Huici. 2014. ClickOS and the art of network function virtualization. In Proceedings of the 11th USENIX Conference on Networked Systems Design and Implementation. USENIX Association, 459--473.

Digital Library

[17]

Sanjeev Mohindra, Daniel Hook, Andrew Prout, Ai-Hoa Sanh, An Tran, and Charles Yee. 2013. Big Data Analysis using Distributed Actors Framework. In Proc. of the 2013 IEEE High Performance Extreme Computing Conference (HPEC).

[18]

Andrew Newell, Gabriel Kliot, Ishai Menache, Aditya Gopalan, Soramichi Akiyama, and Mark Silberstein. 2016. Optimizing Distributed Actor Systems for Dynamic Interactive Services. In Proc. of the Eleventh European Conference on Computer Systems (EuroSys'16).

Digital Library

[19]

CUDA Nvidia. 2011. C programming guide version 4.0. Nvidia Corporation (2011).

[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]

Weibin Sun and Robert Ricci. 2013. Fast and lexible: Parallel packet processing with GPUs and Click. In Proceedings of the ninth ACM/IEEE symposium on Architectures for networking and communications systems. IEEE Press, 25--36.

Digital Library

[22]

Janet Tseng, Ren Wang, James Tsai, Saikrishna Edupuganti, Alexander W Min, Shinae Woo, Stephen Junkins, and Tsung-Yuan Charlie Tai. 2016. Exploiting integrated GPUs for network packet processing workloads. In NetSoft Conference and Workshops (NetSoft), 2016 IEEE. IEEE, 161--165.

Cited By

Zeng DZhu AGu LLi PChen QGuo M(2023)Enabling Efficient Spatio-Temporal GPU Sharing for Network Function VirtualizationIEEE Transactions on Computers10.1109/TC.2023.327854172:10(2963-2977)Online publication date: Oct-2023
https://doi.org/10.1109/TC.2023.3278541
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
Adoga HPezaros D(2022)Network Function Virtualization and Service Function Chaining Frameworks: A Comprehensive Review of Requirements, Objectives, Implementations, and Open Research ChallengesFuture Internet10.3390/fi1402005914:2(59)Online publication date: 15-Feb-2022
https://doi.org/10.3390/fi14020059
Show More Cited By

Index Terms

GPUNFV: a GPU-Accelerated NFV System
1. Networks
  1. Network architectures
    1. Network design principles
      1. Layering

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cover image ACM Other conferences

APNet '17: Proceedings of the First Asia-Pacific Workshop on Networking

August 2017

127 pages

ISBN:9781450352444

DOI:10.1145/3106989

Copyright © 2017 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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Published: 03 August 2017

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APNet'17

APNet'17: First Asia-Pacific Workshop on Networking

August 3 - 4, 2017

Hong Kong, China

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Cited By

Zeng DZhu AGu LLi PChen QGuo M(2023)Enabling Efficient Spatio-Temporal GPU Sharing for Network Function VirtualizationIEEE Transactions on Computers10.1109/TC.2023.327854172:10(2963-2977)Online publication date: Oct-2023
https://doi.org/10.1109/TC.2023.3278541
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
Adoga HPezaros D(2022)Network Function Virtualization and Service Function Chaining Frameworks: A Comprehensive Review of Requirements, Objectives, Implementations, and Open Research ChallengesFuture Internet10.3390/fi1402005914:2(59)Online publication date: 15-Feb-2022
https://doi.org/10.3390/fi14020059
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:COnline publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1016/j.jnca.2022.103480
Xu TSun HZhang DZhou XSui XWang SHuang QBao Y(2022)NfvInsight: A Framework for Automatically Deploying and Benchmarking VNF ChainsJournal of Computer Science and Technology10.1007/s11390-020-0434-137:3(680-698)Online publication date: 1-Jun-2022
https://dl.acm.org/doi/10.1007/s11390-020-0434-1
Farshin ABarbette TRoozbeh AMaguire Jr. GKostić DSherwood TBerger EKozyrakis C(2021)PacketMill: toward per-Core 100-Gbps networkingProceedings of the 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems10.1145/3445814.3446724(1-17)Online publication date: 19-Apr-2021
https://dl.acm.org/doi/10.1145/3445814.3446724
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: 1-Mar-2021
https://dl.acm.org/doi/10.1109/TNSM.2020.3045381
Xu ZZhang ZLui JLiang WXia QZhou PXu WWu G(2021)Affinity-Aware VNF Placement in Mobile Edge Clouds via Leveraging GPUsIEEE Transactions on Computers10.1109/TC.2020.304162970:12(2234-2248)Online publication date: 1-Dec-2021
https://dl.acm.org/doi/10.1109/TC.2020.3041629
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