Energy Aware Scheduling based on Two-phase Frequency Scaling for Parallel Tasks in Cluster

Abstract

Improving the energy efficiency of high performance clusters has become important research issue. We proposed a new algorithm that reduces energy consumption of precedence constrained parallel tasks in power-scalable clusters. To reduce energy consumption without increasing the schedule length, our algorithm reclaims both static and dynamic slack time and employs different frequency adjusting techniques in different slack time. The optimal frequency is obtained through analyzing the precedence constraints of parallel tasks. We conducted extensive experiments to compare the proposed algorithm with two other existing algorithms, simulation results show that the proposed algorithm can get better energy efficiency without increasing the make span.