云中对截止日期敏感作业的推测执行优化

Proceedings of the 2017 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems Pub Date : 2017-06-05 DOI:10.1145/3078505.3078541

Maotong Xu, Sultan Alamro, Tian Lan, S. Subramaniam

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引用次数: 9

摘要

在本文中，我们将各种推测调度策略放在一个统一的优化框架下，该框架定义了一个新的度量，即截止日期前完成概率(PoCD)，以衡量MapReduce作业满足预期截止日期的概率。提出了一个优化问题，对不同策略下的PoCD和执行成本进行联合优化。在Hadoop MapReduce上对三种策略进行了原型化，并通过实验对两种基线策略进行了评估。净效用增加78%，PoCD提高94%，成本降低12%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Optimizing Speculative Execution of Deadline-Sensitive Jobs in Cloud

In this paper, we bring various speculative scheduling strategies together under a unifying optimization framework, which defines a new metric, Probability of Completion before Deadlines (PoCD), to measure the probability that MapReduce jobs meet their desired deadlines. We propose an optimization problem to jointly optimize PoCD and execution cost in different strategies. Three strategies are prototyped on Hadoop MapReduce and evaluated against two baseline strategies using experiments. A 78% net utility increase with up to 94% PoCD and 12% cost improvement is achieved.

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Proceedings of the 2017 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems

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