With the rapid growth of grid computing, more and more data-intensive applications have been deployed in grid environments, which in turn increase the energy consumption in high-performance computing platforms. To address the issue of energy consumption optimisation when scheduling data-intensive workflows, a novel heuristic policy called 'minimal energy consumption path' is proposed. By using this heuristic, we devise two energy-aware algorithms which are deprived from two classical 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 adaptivity than conventional scheduling algorithms, especially when the system is in presence of large-scale workflows which involve highly intensive data-accessing operations.
['Peng Xiao', 'Zhongxiao Hao']
Improving energy-efficiency of large-scale workflows in heterogeneous systems