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Cluster computing applications like MapReduce and Dryad transfer massive amounts of data between their computation stages. These transfers can have a significant impact on job performance, accounting for more than 50% of job completion times. Despite this impact, there has been relatively little work on optimizing the performance of these data transfers. In this paper, the authors propose global management architecture and a set of algorithms that improve the transfer times of common communication patterns, such as broadcast and shuffle, and allow one to prioritize a transfer over other transfers belonging to the same application or to different ones.
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