Failure Recovery in Cooperative Data Stream Analysis

Bin Rong, F. Douglis, Cathy H. Xia, Zhen Liu
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引用次数: 7

Abstract

We present a failure recovery framework for System S, a large-scale stream data analysis environment. It is intended to support multiple sites, which have their own local administration and goals. However, it is beneficial for these sites to cooperate with each other, especially in the presence of various failures. Our ultimate goal is to support automatic, timely failure recovery through cooperation among sites. We identify the unique challenges in the context of System S and present our initial design work. In particular, we consider a backup selection problem, specifying where to recover failed jobs, which we formulate as an optimization problem. We present an approximation algorithm together with empirical results obtained through simulations. Our numerical evaluations show that the proposed approximation algorithm is very efficient and effective compared to the optimal solutions. It exhibits a promising empirical performance ratio that is close to the theoretical limit of polynomial approximations of such a problem
协同数据流分析中的故障恢复
我们提出了一个适用于大规模流数据分析环境System S的故障恢复框架。它旨在支持多个站点,这些站点有自己的本地管理和目标。然而,这些站点相互合作是有益的,特别是在存在各种故障的情况下。我们的最终目标是通过站点之间的合作来支持自动、及时的故障恢复。我们确定了系统S背景下的独特挑战,并介绍了我们的初步设计工作。特别地,我们考虑了一个备份选择问题,指定在哪里恢复失败的作业,我们将其表述为优化问题。本文给出了一种近似算法,并给出了仿真结果。我们的数值计算表明,与最优解相比,所提出的近似算法是非常有效的。它显示了一个有希望的经验性能比,接近这类问题的多项式近似的理论极限
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