Online parameter tuning of the flow classification method in the energy-efficient data center network HOLST

Masaki Murakami, Hiroki Kubokawa, Kyosuke Sugiura, Eiji Oki, Satoru Okamoto, Naoaki Yamanaka

Research output: Contribution to journalArticle

Abstract

An online parameter-tuning method for the energy-efficient data center network (DCN) named HOLST (high-speed optical layer 1 switch system for time-slot-switching-based optical data center networks) is proposed. HOLST comprises an optical circuit switching network, optical slot switching network, and electrical packet switching network for the spine layer. It requires flows to be assigned to optimal switches to achieve high power savings. In this study, first we experimentally confirm that the change in the DCN characteristics in the short term of actual data center traffic downgrades the accuracy of flow classification. Subsequently, we propose a procedure for reconfiguring a flow classification function and a method for online parameter tuning for this function. Finally, the accuracy of the flow classification method using the proposed tuning and estimated switching energy consumption in the spine layer are evaluated. The proposed online parameter-tuning function increases the accuracy of flow classification and reduces switching energy consumption relative to the conventional flow classification function.

Original languageEnglish
Pages (from-to)344-354
Number of pages11
JournalJournal of Optical Communications and Networking
Volume12
Issue number11
DOIs
Publication statusPublished - 2020 Nov

ASJC Scopus subject areas

  • Computer Networks and Communications

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