IntegrityMR:探索大数据计算应用的结果完整性保证解决方案

Int. J. Networked Distributed Comput. Pub Date : 2016-03-31 DOI:10.2991/ijndc.2016.4.2.5

Yongzhi Wang, Jinpeng Wei, M. Srivatsa, Yucong Duan, Wencai Du

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引用次数: 1

摘要

MapReduce应用程序在公共云上的大规模采用受到了对部署在公共云上的参与虚拟机缺乏信任的阻碍。在本文中，我们提出了一种基于多公共云架构的解决方案IntegrityMR，它在任务层和应用层两个可选层执行基于mapreduce的结果完整性检查技术。我们的实验结果表明，两层的解决方案都提供了高的结果完整性，但不可忽略的性能开销。

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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IntegrityMR: Exploring Result Integrity Assurance Solutions for Big Data Computing Applications

Large-scale adoption of MapReduce applications on public clouds is hindered by the lack of trust on the participating virtual machines deployed on the public cloud. In this paper, we propose IntegrityMR, a multi-public clouds architecture-based solution, which performs the MapReduce-based result integrity check techniques at two alternative layers: the task layer and the application layer. Our experimental results show that solutions in both layers offer a high result integrity but non-negligible performance overheads.

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Int. J. Networked Distributed Comput.

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