学会自我恢复

12th IFIP/IEEE International Symposium on Integrated Network Management (IM 2011) and Workshops Pub Date : 2011-05-23 DOI:10.1109/INM.2011.5990506

Thomas Reidemeister, Miao Jiang, Paul A. S. Ward

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引用次数: 1

摘要

商业上的成功取决于可靠而又负担得起的软件系统;这意味着需要基于云的自恢复组件软件系统。在之前的工作中，我们展示了一个离散控制器，它允许基于不确定故障知识调度恢复动作。这种方法需要对历史故障数据进行详细分析。在本文中，我们通过主动探索来研究自适应学习，并论证了漂移或无效知识对恢复行动的影响。

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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Learning to self-recover

Business success is contingent on dependable, yet affordable, software systems; this implies a need for self-recovering cloud-based component software systems. In prior work we demonstrated a discrete controller that allows scheduling of recovery actions based on uncertain fault knowledge. That approach required detailed analysis of historic failure data. In this paper we examine adaptive learning through active exploration and demonstrate the impact of drifting or invalid knowledge about recovery actions.

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来源期刊

12th IFIP/IEEE International Symposium on Integrated Network Management (IM 2011) and Workshops

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