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Online MapReduce processing on two identical parallel machines

Author

Listed:
  • Jidan Huang

    (Donghua University)

  • Feifeng Zheng

    (Donghua University)

  • Yinfeng Xu

    (Donghua University)

  • Ming Liu

    (Tongji University)

Abstract

In this work we investigate the online over-list MapReduce processing problem on two identical parallel machines, aiming at minimizing the makespan. Jobs are revealed one by one, and each job consists of one map task and one reduce task. The map task can be arbitrarily split and processed on both machines simultaneously, while the reduce task has to be processed on a single machine and it cannot be started unless the map task has been completed. We first show that the general case of the problem reduces to the classical two machine online scheduling model with an optimal competitive ratio of 3/2. For a special case where the map task is at least as long as the reduce task, we prove that no online algorithm can be less than 4/3-competitive. An optimal Greedy algorithm with a matching competitive ratio is proposed as well.

Suggested Citation

  • Jidan Huang & Feifeng Zheng & Yinfeng Xu & Ming Liu, 2018. "Online MapReduce processing on two identical parallel machines," Journal of Combinatorial Optimization, Springer, vol. 35(1), pages 216-223, January.
    • Handle: RePEc:spr:jcomop:v:35:y:2018:i:1:d:10.1007_s10878-017-0167-4
    DOI: 10.1007/s10878-017-0167-4
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    References listed on IDEAS

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    1. Cong Chen & Yinfeng Xu & Yuqing Zhu & Chengyu Sun, 2017. "Online MapReduce scheduling problem of minimizing the makespan," Journal of Combinatorial Optimization, Springer, vol. 33(2), pages 590-608, February.
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