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Self-healing by means of runtime execution profiling

机译:通过运行时执行性能分析进行自我修复

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摘要

A self-healing application brings itself into a stable state after a failure put the software into an unstable state. For such self-healing software application, finding fix for a previously unseen fault is a grand challenge. Asking the user to provide fixes for every fault is bad for productivity, especially when the users are non-savvy in technical aspect of computing. If failure scenarios come into existence, the user wants the runtime environment to handle those situations autonomically. This paper presents a new technique of finding self-healing actions by matching a fault scenario to already established fault models. By profiling and capturing runtime parameters and execution pathways, stable execution models are established and later are used to match with an unstable execution scenario. Experimentation and results are presented that showed that even with additional overheads; this technique can prove beneficial for autonomically healing faults and reliving system administrators from mundane troubleshooting situations.
机译:故障自我修复后,自修复应用程序将进入稳定状态。对于这样的自我修复软件应用程序,找到先前未发现的故障的修复方法是一个巨大的挑战。要求用户为每个故障提供解决方案对生产率不利,尤其是当用户对计算技术方面不了解时。如果出现故障情况,则用户希望运行时环境自动处理这些情况。本文提出了一种通过将故障场景与已经建立的故障模型进行匹配来发现自我修复措施的新技术。通过分析和捕获运行时参数和执行路径,可以建立稳定的执行模型,并在以后用于与不稳定的执行场景进行匹配。实验和结果表明,即使有额外的开销,它也可以实现。事实证明,此技术对于自动修复故障以及使系统管理员摆脱平常的故障排除情况很有用。

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