Balancing reducer workload for skewed data using sampling-based partitioning
Balancing reducer workload for skewed data using sampling-based partitioning
复制标题
使用基于采样的分区来平衡倾斜数据的减速器工作负载
DOI:
10.1016/j.compeleceng.2013.07.001
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发表时间:
2014-02
期刊:
影响因子:
--
通讯作者:
Haifeng Li
中科院分区:
文献类型:
--
作者:
Zhaobin Liu;Changqing Ji;Yuanyuan Li;Haifeng Li
MapReduce has emerged as a popular tool for distributed processing of massive data. However, it is not efficient when handling skewed data and it often leads to reducer load imbalance. In this paper, we address the problem of how to efficiently partition intermediate keys to balance the workload of all reducers when processing skewed data. We present a sampling scheme to compute the approximate distribution of key frequency, estimate the overall distribution and then make a partition scheme in advance. Then, we apply it to map phase of the executing MapReduce job. This work not only provides a load-balanced partition strategy, but also keeps a high performance of synchronous mode of MapReduce. We also propose two partition methods based on sampling results: cluster combination and cluster split combination. The experimental results show that our methods achieve a better time and load balancing results.
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DOI:
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发表时间:
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期刊:
2010 IEEE Second International Conference on Cloud Computing Technology and Science
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