Energy-Efficient Scheduling for Parallel Applications Running on Heterogeneous Clusters

High performance clusters have been widely used to provide amazing computing capability for both commercial and scientific applications. However, huge power consumption has prevented the further application of large-scale clusters. Designing energy-efficient scheduling algorithms for parallel applications running on clusters, especially on the high performance heterogeneous clusters, is highly desirable. In this regard, we propose a novel scheduling strategy called energy efficient task duplication schedule (EETDS for short), which can significantly conserve power by judiciously shrinking communication energy cost when allocating parallel tasks to heterogeneous computing nodes. We present the preliminary simulation results for Gaussian and FFT parallel task models to prove the efficiency of our algorithm.