PerfDB: A Data Management System for Fine-Grained Performance Anomaly Detection

2020 IEEE 6th International Conference on Collaboration and Internet Computing (CIC)(2020)

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摘要
In this work, we present our performance data management system, PerfDB, that we use to study fine-grained performance anomalies like Millibottlenecks. We use it to present the first experimental evidence of a phenomenon we call, “Localized Latency Requests.” These are performance bugs that are part of the long-tail of system latency. We also provide a population study of Very Long Response Time (VLRT) requests, a separate performance anomaly belonging to the latency long tail, being inducing by millibottlenecks through queueing effects.
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关键词
data management,systems performance,anomaly detection,log analysis,data mining
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