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Energy-aware task scheduling in heterogeneous computing environments

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Abstract

Efficient application scheduling is critical for achieving high performance in heterogeneous computing (HC) environments. Because of such importance, there are many researches on this problem and various algorithms have been proposed. Duplication-based algorithms are one kind of well known algorithms to solve scheduling problems, which achieve high performance on minimizing the overall completion time (makespan) of applications. However, they pursuit of the shortest makespan overly by duplicating some tasks redundantly, which leads to a large amount of energy consumption and resource waste. With the growing advocacy for green computing systems, energy conservation has been an important issue and gained a particular interest. An existing technique to reduce energy consumption of an application is dynamic voltage/frequency scaling (DVFS), whose efficiency is affected by the overhead of time and energy caused by voltage scaling. In this paper, we propose a new energy-aware scheduling algorithm with reduced task duplication called Energy-Aware Scheduling by Minimizing Duplication (EAMD), which takes the energy consumption as well as the makespan of an application into consideration. It adopts a subtle energy-aware method to search and delete redundant task copies in the schedules generated by duplication-based algorithms, and it is easier to operate than DVFS, and produces no extra time and energy consumption. This algorithm not only consumes less energy but also maintains good performance in terms of makespan compared with duplication-based algorithms. Two kinds of DAGs, i.e., randomly generated graphs and two real-world application graphs, are tested in our experiments. Experimental results show that EAMD can save up to 15.59 % energy consumption for HLD and HCPFD, two classic duplication-based algorithms. Several factors affecting the performance are also analyzed in the paper.

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Acknowledgements

Thanks are due to three anonymous reviewers for their comments and suggestions on improving the manuscript. This research was partially funded by the Key Program of National Natural Science Foundation of China (Grant No. 61133005), and the National Natural Science Foundation of China (Grant Nos. 90715029, 61070057, 61370095, 61202109), the Cultivation Fund of the Key Scientific and Technical Innovation Project, Ministry of Education of China (Grant No. 708066), the Ph.D. Programs Foundation of Ministry of Education of China (20100161110019) and Project supported by the National Science Foundation for Distinguished Young Scholars of Hunan (12JJ1011).

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Authors and Affiliations

  1. College of Information Science and Engineering, Hunan University, Changsha, Hunan, 410082, China

    Jing Mei, Kenli Li & Keqin Li

  2. National Supercomputing Center in Changsha, Changsha, Hunan, 410082, China

    Kenli Li

  3. Department of Computer Science, State University of New York, New Paltz, NY, 12561, USA

    Keqin Li

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  1. Jing Mei
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Correspondence to Kenli Li.

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Mei, J., Li, K. & Li, K. Energy-aware task scheduling in heterogeneous computing environments. Cluster Comput 17, 537–550 (2014). https://doi.org/10.1007/s10586-013-0297-0

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