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Volume 37 Issue 3
May  2022
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Tian-Ni Xu, Hai-Feng Sun, Di Zhang, Xiao-Ming Zhou, Xiu-Feng Sui, Sa Wang, Qun Huang, Yun-Gang Bao. NfvInsight: A Framework for Automatically Deploying and Benchmarking VNF Chains[J]. Journal of Computer Science and Technology, 2022, 37(3): 680-698. DOI: 10.1007/s11390-020-0434-1
Citation: Tian-Ni Xu, Hai-Feng Sun, Di Zhang, Xiao-Ming Zhou, Xiu-Feng Sui, Sa Wang, Qun Huang, Yun-Gang Bao. NfvInsight: A Framework for Automatically Deploying and Benchmarking VNF Chains[J]. Journal of Computer Science and Technology, 2022, 37(3): 680-698. DOI: 10.1007/s11390-020-0434-1
shu

NfvInsight: A Framework for Automatically Deploying and Benchmarking VNF Chains

  • 1State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
    2University of Chinese Academy of Sciences, Beijing 100049, China
    3School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
    4Peng Cheng Laboratory, Shenzhen 518055, China
    5Department of Computer Science and Technology, Peking University, Beijing 100871, China

Funds: This work was supported by the National Key Research and Development Program of China under Grant No. 2019YFB1802600, the National Natural Science Foundation of China under Grant Nos. 61420106013, 61702480, 61672499, and 61802365, the Youth Innovation Promotion Association of Chinese Academy of Sciences under Grant Nos. 2013073 and 2020105, and the Guangdong Province Key Laboratory of Popular High Performance Computers under Grant No. 2017B030314073.
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  • Author Bio:

    Yun-Gang Bao received his B.S. degree from Nanjing University, Nanjing, in 2003, and his Ph.D. degree in computer science from Institute of Computing Technology (ICT), Chinese Academy of Sciences (CAS), Beijing, in 2008. He is a professor at ICT, CAS, Beijing. From 2010 to 2012, he was a postdoctoral researcher at Department of Computer Science, Princeton University, New York city. His current research interests include computer architecture, operating system, and system performance modeling and evaluation. He is a senior member of CCF and a member of ACM and IEEE.

  • Corresponding author:

    Yun-Gang Bao E-mail: baoyg@ict.ac.cn

  • Received Date: March 09, 2020
  • Revised Date: August 18, 2020
  • Accepted Date: October 14, 2020
  • Published Date: May 29, 2022
    Abstract
  • With the advent of virtualization techniques and software-defined networking (SDN), network function virtualization (NFV) shifts network functions (NFs) from hardware implementations to software appliances, between which exists a performance gap. How to narrow the gap is an essential issue of current NFV research. However, the cumbersomeness of deployment, the water pipe effect of virtual network function (VNF) chains, and the complexity of the system software stack together make it tough to figure out the cause of low performance in the NFV system. To pinpoint the NFV system performance, we propose NfvInsight, a framework for automatic deployment and benchmarking VNF chains. Our framework tackles the challenges in NFV performance analysis. The framework components include chain graph generation, automatic deployment, and fine granularity measurement. The design and implementation of each component have their advantages. To the best of our knowledge, we make the first attempt to collect rules forming a knowledge base for generating reasonable chain graphs. NfvInsight deploys the generated chain graphs automatically, which frees the network operators from executing at least 391 lines of bash commands for a single test. To diagnose the performance bottleneck, NfvInsight collects metrics from multiple layers of the software stack. Specifically, we collect the network stack latency distribution ingeniously, introducing only less than 2.2% overhead. We showcase the convenience and usability of NfvInsight in finding bottlenecks for both VNF chains and the underlying system. Leveraging our framework, we find several design flaws of the network stack, which are unsuitable for packet forwarding inside one single server under the NFV circumstance. Our optimization for these flaws gains at most 3x performance improvement.
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