MapReduce中重叠相位的联合优化

Minghong Lin, Li Zhang, A. Wierman, Jian Tan
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引用次数: 97

摘要

MapReduce是一个用于大数据处理的可扩展并行计算框架。它具有多个处理阶段,因此有效的作业调度机制对于确保有效的资源利用至关重要。这项工作研究了由于MapReduce中“map”和“shuffle”阶段重叠而导致的调度挑战。我们提出了一个新的、通用的调度问题模型。进一步,我们证明了该模型中最小化平均响应时间的调度在离线情况下是强np困难的,并且在在线情况下没有在线算法可以持续竞争。然而,我们提供了两种在线算法,当给定略快的服务速率时,它们的性能与离线最优算法相匹配。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Joint optimization of overlapping phases in MapReduce
MapReduce is a scalable parallel computing framework for big data processing. It exhibits multiple processing phases, and thus an efficient job scheduling mechanism is crucial for ensuring efficient resource utilization. This work studies the scheduling challenge that results from the overlapping of the "map" and "shuffle" phases in MapReduce. We propose a new, general model for this scheduling problem. Further, we prove that scheduling to minimize average response time in this model is strongly NP-hard in the offline case and that no online algorithm can be constant-competitive in the online case. However, we provide two online algorithms that match the performance of the offline optimal when given a slightly faster service rate.
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