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• W4251459357 abstract "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, with networking researchers traditionally focusing on per-flow traffic management. We address this limitation by proposing a global management architecture and a set of algorithms that (1) improve the transfer times of common communication patterns, such as broadcast and shuffle, and (2) allow scheduling policies at the transfer level, such as prioritizing a transfer over other transfers. Using a prototype implementation, we show that our solution improves broadcast completion times by up to 4.5X compared to the status quo in Hadoop. We also show that transfer-level scheduling can reduce the completion time of high-priority transfers by 1.7X." @default.
• W4251459357 created "2022-05-12" @default.
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• W4251459357 date "2011-08-15" @default.
• W4251459357 modified "2023-10-04" @default.
• W4251459357 title "Managing data transfers in computer clusters with orchestra" @default.
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• W4251459357 doi "https://doi.org/10.1145/2043164.2018448" @default.
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