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MapReduce has emerged as a popular computing model for parallel processing of big data. However, we observe that the native hash partitioning of MapReduce systems leads to frequent uneven data distribution among reduce tasks. The uneven data distribution results in load imbalance among reduce tasks, and thus hampers the performance of MapReduce systems. Moreover, the heterogeneity among cluster nodes...
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