Minimizing makespan and total completion time in MapReduce-like systems

Yuqing Zhu, Yiwei Jiang, Weili Wu, Ling Ding, Ankur Teredesai, Deying Li, Wonjun Lee

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    41 Citations (Scopus)


    Effectiveness of MapReduce as a big data processing framework depends on efficiencies of scale for both map and reduce phases. While most map tasks are preemptive and parallelizable, the reduce tasks typically are not easily decomposed and often become a bottleneck due to constraints of data locality and task complexity. By assuming that reduce tasks are non-parallelizable, we study offline scheduling of minimizing makespan and minimizing total completion time, respectively. Both preemptive and non-preemptive reduce tasks are considered. On makespan minimization, for preemptive version we design an algorithm and prove its optimality, for non-preemptive version we design an approximation algorithm with the worst ratio of 3/2-1/2h where h is the number of machines. On total complete time minimization, for non-preemptive version we devise an approximation algorithm with worst case ratio of 2-1/h, and for preemptive version we devise a heuristic. We confirm that our algorithms outperform state-of-art schedulers through experiments.

    Original languageEnglish
    Title of host publicationProceedings - IEEE INFOCOM
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Number of pages9
    ISBN (Print)9781479933600
    Publication statusPublished - 2014 Jan 1
    Event33rd IEEE Conference on Computer Communications, IEEE INFOCOM 2014 - Toronto, ON, Canada
    Duration: 2014 Apr 272014 May 2


    Other33rd IEEE Conference on Computer Communications, IEEE INFOCOM 2014
    CityToronto, ON

    ASJC Scopus subject areas

    • General Computer Science
    • Electrical and Electronic Engineering


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