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Mining Temporal Invariants from Partially Ordered Logs

机译:从部分有序日志中挖掘时间不变量

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A common assumption made in log analysis research is that the underlying log is totally ordered. For concurrent systems, this assumption constrains the generated log to either exclude concurrency altogether, or to capture a particular interleaving of concurrent events. This paper argues that capturing concurrency as a partial order is useful and often indispensable for answering important questions about concurrent systems. To this end, we motivate a family of event ordering invariants over partially ordered event traces, give three algorithms for mining these invariants from logs, and evaluate their scalability on simulated distributed system logs.
机译:日志分析研究中的一个常见假设是底层日志是完全有序的。对于并发系统,此假设将生成的日志约束为完全排除并发,或捕获并发事件的特定交织。本文认为,将并发捕获为部分顺序是有用的,而且对于回答有关并发系统的重要问题通常是必不可少的。为此,我们在部分排序的事件跟踪上激发了一系列事件排序不变式,给出了三种从日志中挖掘这些不变式的算法,并评估了它们在模拟分布式系统日志中的可伸缩性。

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