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When NFV meets ANN: Rethinking elastic scaling for ANN-based NFs

  • Menghao Zhang
  • , Jiasong Bai
  • , Guanyu Li
  • , Zili Meng
  • , Hongda Li
  • , Hongxin Hu
  • , Mingwei Xu
  • Tsinghua University
  • Clemson University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

Network Function Virtualization (NFV) provides middleboxes with substantial elasticity from a system level, and Artificial Neural Network (ANN) empowers middleboxes with great intelligence from an algorithm-level perspective. However, when ANN-based Network Functions (NFs) want to take advantage of the elasticity of NFV, our study finds that huge gaps exist between the existing approaches and the ideal goals for the elasticity control of ANN-based NFs. By revealing the key differences between ANN-based NFs and traditional NFs, we propose LEGO, an innovative framework that provides systematic mechanisms for traffic splitting, instance partition and runtime management to enable correct and efficient scaling of ANN-based NFs. Preliminary implementation and evaluation demonstrate the feasibility and effectiveness of the LEGO system. The major purpose of this paper is to highlight these challenges and sketch out a new roadmap towards ANN-based NFV paradigm.

Original languageEnglish
Title of host publication27th IEEE International Conference on Network Protocols, ICNP 2019
PublisherIEEE Computer Society
ISBN (Electronic)9781728127002
DOIs
StatePublished - Oct 2019
Event27th IEEE International Conference on Network Protocols, ICNP 2019 - Chicago, United States
Duration: Oct 7 2019Oct 10 2019

Publication series

NameProceedings - International Conference on Network Protocols, ICNP
Volume2019-October
ISSN (Print)1092-1648

Conference

Conference27th IEEE International Conference on Network Protocols, ICNP 2019
Country/TerritoryUnited States
CityChicago
Period10/7/1910/10/19

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