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HScheduler: an optimal approach to minimize the makespan of multiple MapReduce jobs

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Abstract

Large-scale MapReduce clusters that routinely process big data bring challenges to the cloud computing. One of the key challenges is to reduce the response time of these MapReduce clusters by minimizing their makespans. It is observed that the order in which these jobs are executed can have a significant impact on their overall makespans and resource utilization. In this work, we consider a scheduling model for multiple MapReduce jobs. The goal is to design a job scheduler that minimizes the makespan of such a set of MapReduce jobs. We exploit classical Johnson model and propose a novel framework HScheduler, which combines features of both classical Johnson’s algorithm and MapReduce to minimize the makespan for both offline and online jobs. Our Offline HScheduler reaches the theoretical lower bound (optimum) and Online HScheduler is 2-competitive which is the best-known constant ratio for minimizing the makespan. Through extensive real data tests, we find that HScheduler has better performance than the best-known approach by 10.6–11.7 % on average for offline scheduling and 8–10 % on average for online scheduling. The HScheduler can be applied to improve responsive time, throughput and energy efficiency in cloud computing.

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Acknowledgments

This research is partially supported by China National Science Foundation (CNSF) with project ID 61450110440 and Sichuan Province Technology Plan (ID 2016GZ0322); Chongqing Research Program of Basic Research and Frontier Technology (ID cstc2015jcyjB0244). Prof. Wenhong Tian finished most of this work when he was a visiting fellow at CLOUDS lab led by Prof. Rajkumar Buyya at the University of Melbourne, Australia. The author thanks team members in CLOUDS for their comments to polish the manuscript.

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Authors and Affiliations

  1. School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China

    Wenhong Tian, Guozhong Li & Wutong Yang

  2. The University of Melbourne, Parkville, Australia

    Rajkumar Buyya

  3. Big Data Research Center at University of Electronic Science and Technology of China, (UESTC), Chengdu, China

    Wenhong Tian

  4. Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, 400714, China

    Wenhong Tian

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  1. Wenhong Tian
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  2. Guozhong Li
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  3. Wutong Yang
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  4. Rajkumar Buyya
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Corresponding author

Correspondence to Wenhong Tian.

Additional information

This research is sponsored by the Natural Science Foundation of China (NSFC) Grant 61450110440.

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Tian, W., Li, G., Yang, W. et al. HScheduler: an optimal approach to minimize the makespan of multiple MapReduce jobs. J Supercomput 72, 2376–2393 (2016). https://doi.org/10.1007/s11227-016-1737-4

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  • DOI: https://doi.org/10.1007/s11227-016-1737-4

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