Energy-Efficient Scheduling

Abstract

AbstractWe review algorithmic techniques for energy conservation in processing environments handling big data sets. Firstly, we address dynamic speed scaling, where processors can run at variable speed/frequency. The goal is to use the speed spectrum of the processors so as to minimize energy consumption while providing a desired service. Here we focus on multi-processor platforms with heterogeneous CPUs. Secondly, we examine power-down mechanisms where idle devices can be transitioned into low-power standby and sleep states. We consider power-down mechanisms in massively parallel systems, where the components have to coordinate their active and idle periods. In particular we focus on data centers with homogeneous as well as heterogeneous servers.