Energy-aware clustering scheduling of parallel applications on heterogeneous computing systems

Energy saving in multiprocessor system is emerging as a primary issue in recent years. Mostly the work available has stressed on reducing makespan of application and little attention has been paid to energy management. In this paper, a novel dynamic power management based clustering task scheduling algorithm is proposed for heterogeneous computing platforms. The prime objective of this paper is to reduce the dynamic and static power consumption of the processors. This paper couples clustering scheduling heuristics with dynamic power management technique to reduce energy consumption. The simulation based results of the proposed energy-aware clustering task scheduling algorithm with dynamic power management are analysed and compare with well-known list based heterogeneous earliest finish time (HEFT) and list based energy-aware precedence-constrained tasks algorithm (PASTA) algorithm. The result carried out for an extensive set of random and real world graphs demonstrate the potency of the proposed algorithm.