Authors: Peng Xiao; Zhongxiao Hao
Addresses: Department of Computer and Communication, Hunan Institute of Engineering, Xiangtan City, China ' School of Information Science and Electrical Engineering, Hebei University of Engineering, Handan City, China
Abstract: 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.
Keywords: grid computing; energy efficiency; quality of service; QoS; resource allocation; heterogeneous systems; energy consumption; high-performance computing; workflow scheduling; data-intensive workflows.
International Journal of Computational Science and Engineering, 2016 Vol.13 No.3, pp.258 - 267
Received: 18 Jan 2014
Accepted: 26 Jun 2014
Published online: 24 Aug 2016 *