Energy efficient scheduling and optimization for parallel tasks on homogeneous clusters

The design of energy-efficient scheduling algorithms has become a hot research topic in high performance computing. To shorten schedule length of parallel tasks with precedence constraints, scheduling algorithms could duplicate tasks on critical paths to avoid communication delay caused by inter-task dependence. However, task duplications incur more energy consumption. In this paper, we propose a heuristic Processor Reduction Optimizing (PRO) approach to reduce the number of processors used to run parallel tasks, thereby decreasing system energy consumption. The PRO approach can find appropriate time slots to accommodate tasks from low-utilized processors according to their earliest start time and earliest complete time. Extensive experimental results show that the proposed PRO approach, compared to existing duplication-based scheduling algorithms, such as Task Duplication Scheduling (TDS), Energy-Aware Duplication (EAD) and Performance-Energy Balanced Duplication (PEBD) algorithms, can effectively decrease the number of used processors and save energy without performance degradation.