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OPTIMA: On-Line Partitioning Skew Mitigation for MapReduce with Resource Adjustment

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Abstract

Partitioning skew has been shown to be a major issue that can significantly prolong the execution time of MapReduce jobs. Most of the existing off-line heuristics for partitioning skew mitigation are inefficient; they have to wait for the completion of all the map tasks. Some solutions can tackle this problem on-line, but will impose an additional overhead by repartitioning the workload of overloaded tasks. In this paper, we present OPTIMA, an on-line partitioning skew mitigation technique for MapReduce. OPTIMA predicts the workload distribution of reduce tasks at run-time, leverages the deviation detection technique to identify the overloaded tasks and pro-actively adjusts resource allocation for these tasks to reduce their execution time. We provide the upper bound of OPTIMA in time complexity, while allowing OPTIMA to perform totally on-line. Through experiments using both real and synthetic workloads running on an 11-node Hadoop cluster, we have observed OPTIMA can effectively mitigate the partitioning skew and improved the job completion time by up to 36.73 % in our experiments.

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Notes

  1. Since the memory requirement is related to the size of data, larger datasets are needed in order to clearly demonstrate the impact of memory allocation.

  2. Using compression in Hadoop to optimize MapReduce performance is prevalent in industry and academia [6, 27, 34].

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Acknowledgments

This work is supported in part by the National Natural Science Foundation of China (No. 61472438), and in part by the Smart Applications on Virtual Infrastructure (SAVI) project funded under the National Sciences and Engineering Research Council of Canada (NSERC) Strategic Networks Grant Number NETGP394424-10.

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Authors and Affiliations

  1. College of Computer, National University of Defense Technology, Changsha, Hunan, China

    Zhihong Liu & Baosheng Wang

  2. David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, Canada

    Qi Zhang & Raouf Boutaba

  3. Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, Changsha, Hunan, China

    Yaping Liu

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  1. Zhihong Liu
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  2. Qi Zhang
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  3. Raouf Boutaba
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  4. Yaping Liu
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  5. Baosheng Wang
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Correspondence to Raouf Boutaba.

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Currently, Zhihong Liu is with University of Waterloo as a visiting student.

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Liu, Z., Zhang, Q., Boutaba, R. et al. OPTIMA: On-Line Partitioning Skew Mitigation for MapReduce with Resource Adjustment. J Netw Syst Manage 24, 859–883 (2016). https://doi.org/10.1007/s10922-015-9362-8

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