When NFV Meets ANN: Rethinking Elastic Scaling for ANN-based NFs

Published in Workshop on Harnessing the Data Revolution in Networking (HDR-Nets), co-located with ICNP, 2019

Recommended citation:
Menghao Zhang, Jiasong Bai, Guanyu Li, Zili Meng, Hongda Li, Hongxin Hu, and Mingwei Xu. "When NFV Meets ANN: Rethinking Elastic Scaling for ANN-based NFs". In the 1st Workshop on Harnessing the Data Revolution in Networking (HDR-Nets), co-located with ICNP 2019, Chicago, Illinois, USA, October 7, 2019.

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.

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