Energy-aware scheduling policy for data-intensive workflow

With the increasing scale of data centers,high energy consumption has become a critical issue in high-performance computing area.To address the issue of energy consumption optimization for data-intensive workflow applications,a set of virtual data-accessing nodes are introduced into the original workflow for quantitatively evaluating the data-accessing energy consumption,by which a novel heuristic policy called minimal energy consumption path is designed.Based on the proposed heuristic policy,two energy-aware scheduling algorithms are implemented,which are deprived from the classical HEFT and CPOP scheduling algorithms.Extensive experiments are conducted to investigate the performance of the proposed algorithms,and the results show that they can significantly reduce the data-accessing energy consumption.Also,the proposed algorithms show better adaptive when the system is in presence of large-scale workflows.