Pfimbi:通过流控数据复制加速大数据作业

Simbarashe Dzinamarira, Florin Dinu, T. Ng
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引用次数: 2

摘要

HDFS的性能对于大数据软件栈来说是至关重要的,并且一直是业界和开源社区最近努力的前沿。一个关键问题是执行数据复制的方式缺乏灵活性。为了解决这个问题,本文介绍了Pfimbi,它是HDFS的第一个替代方案,同时支持同步和流控异步数据复制。Pfimbi有很多优点:它可以加速作业,利用未充分利用的存储I/O带宽,并支持分层存储I/O带宽分配策略。我们证明,对于来自Facebook工作负载的作业跟踪,Pfimbi将平均作业运行时提高了18%,在最好的情况下可提高46%。我们还证明了流控制对于充分利用异步复制的好处至关重要;移除Pfimbi的流量控制机制后,作业运行时间增加了2.7倍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Pfimbi: Accelerating big data jobs through flow-controlled data replication
The performance of HDFS is critical to big data software stacks and has been at the forefront of recent efforts from the industry and the open source community. A key problem is the lack of flexibility in how data replication is performed. To address this problem, this paper presents Pfimbi, the first alternative to HDFS that supports both synchronous and flow-controlled asynchronous data replication. Pfimbi has numerous benefits: It accelerates jobs, exploits under-utilized storage I/O bandwidth, and supports hierarchical storage I/O bandwidth allocation policies. We demonstrate that for a job trace derived from a Facebook workload, Pfimbi improves the average job runtime by 18% and by up to 46% in the best case. We also demonstrate that flow control is crucial to fully exploiting the benefits of asynchronous replication; removing Pfimbi's flow control mechanisms resulted in a 2.7× increase in job runtime.
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