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author:

Chen, Xiang (Chen, Xiang.) [1] | Liu, Hongyan (Liu, Hongyan.) [2] | Zhang, Dong (Zhang, Dong.) [3] (Scholars:张栋) | Meng, Zili (Meng, Zili.) [4] | Huang, Qun (Huang, Qun.) [5] | Zhou, Haifeng (Zhou, Haifeng.) [6] | Wu, Chunming (Wu, Chunming.) [7] | Liu, Xuan (Liu, Xuan.) [8] | Yang, Qiang (Yang, Qiang.) [9]

Indexed by:

EI SCIE

Abstract:

In network function virtualization (NFV), network functions (NFs) are chained as a service function chain (SFC) to enhance NF management with low cost and high flexibility. Recent NFV solutions indicate that the packet processing performance of SFCs can be significantly improved by offloading NFs to programmable switches. However, such offloading requires a deep understanding of heterogeneous NF properties (e.g., NF resource consumption and NF performance behaviors) to achieve the maximum SFC performance. Unfortunately, none of existing solutions provide automatic analysis of these NF properties. Thus, network administrators have to manually examine the source codes of NFs and profile various NF properties by hand, which is extremely time-consuming and laborious. In this article, we propose LightNF, a novel system that simplifies NF offloading in programmable networks. LightNF automatically dissects comprehensive NF properties by means of code analysis and performance profiling while eliminating manual efforts. It then leverages its analysis results of NF properties in its SFC placement so as to make the performance-optimal offloading decisions. We have implemented LightNF on Tofino-based hardware programmable switches. We perform extensive experiments to evaluate LightNF with a real-world testbed and large-scale simulation. Our experiments show that LightNF outperforms existing solutions with an orders-of-magnitude reduction in per-packet processing latency and 9.5x improvement in SFC throughput.

Keyword:

Network functions packet processing performance programmable switches service function chains

Community:

  • [ 1 ] [Chen, Xiang]Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou 310007, Zhejiang, Peoples R China
  • [ 2 ] [Liu, Hongyan]Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou 310007, Zhejiang, Peoples R China
  • [ 3 ] [Wu, Chunming]Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou 310007, Zhejiang, Peoples R China
  • [ 4 ] [Zhang, Dong]Fuzhou Univ, Coll Comp Sci & Big Data, Fuzhou 350116, Fujian, Peoples R China
  • [ 5 ] [Meng, Zili]Tsinghua Univ, Inst Network Sci & Cyberspace, Beijing 100084, Peoples R China
  • [ 6 ] [Huang, Qun]Peking Univ, Sch Elect Engn & Comp Sci, Beijing 100871, Peoples R China
  • [ 7 ] [Zhou, Haifeng]Zhejiang Univ, Coll Control Sci & Engn, Hangzhou 310007, Zhejiang, Peoples R China
  • [ 8 ] [Liu, Xuan]Yangzhou Univ, Coll Informat Engn, Coll Artificial Intelligence, Yangzhou 225012, Jiangsu, Peoples R China
  • [ 9 ] [Liu, Xuan]Southeast Univ, Sch Comp Sci & Engn, Nanjing 211189, Jiangsu, Peoples R China
  • [ 10 ] [Yang, Qiang]Zhejiang Univ, Coll Elect Engn, Hangzhou 310007, Zhejiang, Peoples R China

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Source :

IEEE TRANSACTIONS ON CLOUD COMPUTING

ISSN: 2168-7161

Year: 2023

Issue: 2

Volume: 11

Page: 1591-1607

5 . 3

JCR@2023

5 . 3 0 0

JCR@2023

ESI Discipline: COMPUTER SCIENCE;

ESI HC Threshold:32

JCR Journal Grade:1

CAS Journal Grade:2

Cited Count:

WoS CC Cited Count: 4

SCOPUS Cited Count: 5

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 0

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