PBSE:数据并行框架中退化网络尾部容忍度的稳健的基于路径的推测执行

Riza O. Suminto, Cesar A. Stuardo, Alexandra Clark, Huan Ke, Tanakorn Leesatapornwongsa, Bo Fu, D. Kurniawan, V. Martin, Maheswara Rao G. Uma, Haryadi S. Gunawi
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引用次数: 20

摘要

我们在一个独特的故障模型下揭示了推测执行(SE)实现的漏洞:节点级网络吞吐量退化。这个问题出现在许多数据并行框架中,如Hadoop MapReduce和Spark。为了解决这个问题,我们提出了PBSE,这是一种强大的、基于路径的推测执行,它采用了三个关键成分:路径进度、路径多样性和路径离散者检测和推测。我们展示了在上述故障模型下,PBSE如何优于克隆和积极猜测等其他方法。PBSE是一种通用解决方案,适用于Hadoop/HDFS+QFS、Spark、Flume等多种数据并行框架。
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PBSE: a robust path-based speculative execution for degraded-network tail tolerance in data-parallel frameworks
We reveal loopholes of Speculative Execution (SE) implementations under a unique fault model: node-level network throughput degradation. This problem appears in many data-parallel frameworks such as Hadoop MapReduce and Spark. To address this, we present PBSE, a robust, path-based speculative execution that employs three key ingredients: path progress, path diversity, and path-straggler detection and speculation. We show how PBSE is superior to other approaches such as cloning and aggressive speculation under the aforementioned fault model. PBSE is a general solution, applicable to many data-parallel frameworks such as Hadoop/HDFS+QFS, Spark and Flume.
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