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

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.