异构Hadoop系统中基于节点分类的智能推测执行策略

Qi Liu, Weidong Cai, Jian Shen, Zhangjie Fu, N. Linge
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引用次数: 3

摘要

MapReduce (MR)被广泛用于处理分布式大数据集。同时,推测执行是一种通过将运行在性能较低的机器上的任务备份到性能较高的机器上来处理相同问题的方法。在本文中,我们修正了一些缺陷,并考虑了异构环境。我们还在Hadoop-2.6中基于节点分类实现了该策略,该策略称为Speculation-NC,优化后的Hadoop称为Hadoop- nc。实验结果表明,与传统备份策略相比,该方法可以正确地备份任务,提高了MRV2的性能,减少了执行时间和资源消耗。
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A smart speculative execution strategy based on node classification for heterogeneous Hadoop systems
MapReduce (MR) has been widely used to process distributed large data sets. Meanwhile, speculative execution is known as an approach for dealing with same problems by backing up those tasks running on a low performance machine to a higher one. In this paper, we have modified some pitfalls and taken heterogeneous environment into consideration. We also have implemented it in Hadoop-2.6 based on node classification, this strategy is called Speculation-NC and optimized Hadoop is called Hadoop-NC. Experiment results show that our method can correctly backup a task, improve the performance of MRV2 and decrease the execution time and resource consumption compared with traditional strategy.
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