On Traffic-Aware Partition and Aggregation in MapReduce for Big Data Applications
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On Traffic-Aware Partition and Aggregation in MapReduce for Big Data Applications
Abstract— On Traffic-Aware Partition and Aggregation in MapReduce for Big Data Applications. The MapReduce programming model simplifies large-scale data processing on commodity cluster by exploiting parallel map tasks and reduce tasks. Although many efforts have been made to improve the performance of MapReduce jobs, they ignore the network traffic generated in the shuffle phase, which plays a critical role in performance enhancement < Final Year Projects 2016 > improvement. Traditionally, a hash function is used to partition intermediate data among reduce tasks, which, however, is not traffic-efficient because network topology and data size associated with each key are not taken into consideration. In this paper, we study to reduce network traffic cost for a MapReduce job by designing a novel intermediate data partition scheme.
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