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A Profit-Maximum Resource Allocation Approach for Mapreduce in Data Centers

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Green, Pervasive, and Cloud Computing (GPC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10232))

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Abstract

Resource allocation for Mapreduce data processing poses difficult challenges to system administrators in data centers. The extreme scale of Mapreduce applications require an efficiently profitable resource allocation algorithm that minimizes the energy consumption cost while maintaining the highest level of performance. In this paper, we propose a profit-maximum model that minimizes the cost of energy consumption and makespan. By adopting a minimum-weight b-matching rounding algorithm (MBRA) to find an integer solution, then assign map/reduce tasks to individual slots to build a complete resource allocation. Finally, we perform experiments on real workload to evaluate the profit-maximum model and analyze the performance of our proposed algorithm. The results show that MBRA is able to find a near-optimal integer solution that maximizes the profit per unit time in a lower runtime, and it is up to 30%~70% in profit that is better than the current heuristic scheduling algorithm and the rounding algorithm.

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Acknowledgments

This paper was supported by the National Natural Science Foundation of China (Nos. 61170222, 61662088, 11301466), the Natural Science Foundation of Yunnan Province of China (No. 2014FB114), and the Scientific Research Foundation of the Educational Department of Yunnan Province (No. 2015J0007).

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Authors and Affiliations

  1. School of Information Science and Engineering, Yunnan University, Kunming, China

    Xiaolu Zhang, Weidong Li, Xi Liu & Xuejie Zhang

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  1. Xiaolu Zhang
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  2. Weidong Li
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  3. Xi Liu
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  4. Xuejie Zhang
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Corresponding author

Correspondence to Xuejie Zhang .

Editor information

Editors and Affiliations

  1. Hong Kong Polytechnic University, Hong Kong, Hong Kong

    Man Ho Allen Au

  2. University of Salerno, Fisciano, Italy

    Arcangelo Castiglione

  3. University of Texas at San Antonio, San Antonio, Texas, USA

    Kim-Kwang Raymond Choo

  4. University of Salerno, Fisciano, Italy

    Francesco Palmieri

  5. Providence University, Taichung City, Taiwan

    Kuan-Ching Li

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Zhang, X., Li, W., Liu, X., Zhang, X. (2017). A Profit-Maximum Resource Allocation Approach for Mapreduce in Data Centers. In: Au, M., Castiglione, A., Choo, KK., Palmieri, F., Li, KC. (eds) Green, Pervasive, and Cloud Computing. GPC 2017. Lecture Notes in Computer Science(), vol 10232. Springer, Cham. https://doi.org/10.1007/978-3-319-57186-7_34

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  • DOI: https://doi.org/10.1007/978-3-319-57186-7_34

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  • Print ISBN: 978-3-319-57185-0

  • Online ISBN: 978-3-319-57186-7

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