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Scientific Programming
Volume 2016 (2016), Article ID 9823213, 13 pages
Research Article

Energy-Efficient Reliability-Aware Scheduling Algorithm on Heterogeneous Systems

1School of Information Science and Engineering, National Supercomputing Center in Changsha, Hunan University, Changsha 410082, China
2Information Science and Technology College/Southern Regional Collaborative Innovation Center for Grain and Oil Crops in China, Hunan Agricultural University, Changsha 410128, China
3Archive, Hunan University of Humanities, Science and Technology, Loudi 417000, China

Received 22 December 2015; Accepted 24 February 2016

Academic Editor: Florin Pop

Copyright © 2016 Xiaoyong Tang and Weizhen Tan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The amount of energy needed to operate high-performance computing systems increases regularly since some years at a high pace, and the energy consumption has attracted a great deal of attention. Moreover, high energy consumption inevitably contains failures and reduces system reliability. However, there has been considerably less work of simultaneous management of system performance, reliability, and energy consumption on heterogeneous systems. In this paper, we first build the precedence-constrained parallel applications and energy consumption model. Then, we deduce the relation between reliability and processor frequencies and get their parameters approximation value by least squares curve fitting method. Thirdly, we establish a task execution reliability model and formulate this reliability and energy aware scheduling problem as a linear programming. Lastly, we propose a heuristic Reliability-Energy Aware Scheduling (REAS) algorithm to solve this problem, which can get good tradeoff among system performance, reliability, and energy consumption with lower complexity. Our extensive simulation performance evaluation study clearly demonstrates the tradeoff performance of our proposed heuristic algorithm.