ScaleFlux : Efficient Stateful Scaling in NFV

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)22_Publication in policy or professional journalpeer-review

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Author(s)

  • Libin Liu
  • Hong Xu
  • Zhixiong Niu
  • Wei Zhang
  • Peng Wang

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)4801-4817
Journal / PublicationIEEE Transactions on Parallel and Distributed Systems
Volume33
Issue number12
Online published5 Sep 2022
Publication statusPublished - Dec 2022

Abstract

Network function virtualization (NFV) enables elastic scaling to middlebox deployment and management. Therefore, efficient stateful scaling is an important task because operators often need to shift traffic and the associated flow states across VNF instances to deal with time-varying loads. Existing NFV scaling methods, however, typically focus on one aspect of the scaling pipeline and does not offer an end-to-end scaling framework. This article presents ScaleFlux, a complete stateful scaling system that efficiently reduces flow-level latency and achieves near-optimal resource usage. ScaleFlux (1) monitors traffic load for each VNF instance and adopts a queue-based mechanism to detect load burstiness timely, (2) deploys a flow bandwidth predictor to predict flow bandwidth time-series with the ABCNN-LSTM model, and (3) schedules the necessary flow and state migration using the simulated annealing algorithm to achieve both flow-level latency guarantee and resource usage minimization. Testbed evaluation with a five-machine cluster shows that ScaleFlux reduces flow completion time by at least 8.7× for all the workloads and achieves near-optimal CPU usage during scaling.

Research Area(s)

  • Network function virtualization, network load detection, flow bandwidth prediction, stateful scaling, service level agreements

Citation Format(s)

ScaleFlux : Efficient Stateful Scaling in NFV. / Liu, Libin; Xu, Hong; Niu, Zhixiong; Li, Jingzong; Zhang, Wei; Wang, Peng; Li, Jiamin; Xue, Jason Chun; Wang, Cong.

In: IEEE Transactions on Parallel and Distributed Systems, Vol. 33, No. 12, 12.2022, p. 4801-4817.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)22_Publication in policy or professional journalpeer-review