Volume 7 Number 12 (Dec. 2012)
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JCP 2012 Vol.7(12): 2962-2970 ISSN: 1796-203X
doi: 10.4304/jcp.7.12.2962-2970

Energy-efficient task scheduling model based on MapReduce for cloud computing using genetic algorithm

Xiaoli Wang1, Yuping Wang1, Hai Zhu2
1School of Computer Science and Technology, Xidian University, Xi’an 710071, China
2School of Computer Science and Technology, Zhoukou Normal University, Zhoukou 466001, China

Abstract—High energy consumption of data centers has become a great obstacle to the development of cloud computing. This paper mainly focuses on how to improve the energy efficiency of servers in a data center by appropriate task scheduling strategies. Based on MapReduce, Google’s massive data processing framework, a new energy-efficient task scheduling model is proposed in this paper. To solve this model, we put forward an effective genetic algorithm with practical encoding and decoding methods and specially designed genetic operators. Meanwhile, with a view to accelerating this algorithm’s convergent speed as well as enhancing its searching ability, a local search operator is introduced. Finally, the experiments show that the proposed algorithm is effective and efficient.

Index Terms—Energy-efficient task scheduling, Cloud computing, MapReduce, Genetic algorithm.

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Cite: Xiaoli Wang, Yuping Wang, Hai Zhu, "Energy-efficient task scheduling model based on MapReduce for cloud computing using genetic algorithm," Journal of Computers vol. 7, no. 12, pp. 2962-2970, 2012.

General Information

ISSN: 1796-203X
Abbreviated Title: J.Comput.
Frequency: Bimonthly
Editor-in-Chief: Prof. Liansheng Tan
Executive Editor: Ms. Nina Lee
Abstracting/ Indexing: DBLP, EBSCO,  ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, CNKI,etc
E-mail: jcp@iap.org
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