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- W3096738151 abstract "Abstract More and more developers and companies have adopted the concept of microservice. Detecting anomalies and locating root causes are important for improving the reliability of microservices. Current approaches typically monitor the metrics of physical resources, and manually set alarm rules. However, they often require domain knowledge to detect anomalies, and cannot locate faulty microservices causing the anomalies accurately. To address the above issues, we propose an anomaly detection approach for microservice application by comparing execution traces. First, we use dynamic instrumentations to collect execution traces across microservices, and then use call trees to describe an application’s execution traces. Then, we calculate the anomaly degree of traces with tree edit distance to detect structural anomalies, and then analyze the difference between traces to locate the components causing the anomalies. Third, we locate suspicious component calls causing the response time fluctuation with principal component analysis to detect response time anomalies. Finally, we have evaluated the approach with a TPC-W based benchmark called as Bench4Q and a typical microservice-based application called as Social Network. The results demonstrate that the approach achieves 81%–97% precision and 75%–99% recall in detecting anomalies caused by injected CPU, network, memory and service faults." @default.
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- W3096738151 date "2021-03-01" @default.
- W3096738151 modified "2023-10-05" @default.
- W3096738151 title "Detecting anomalies in microservices with execution trace comparison" @default.
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- W3096738151 doi "https://doi.org/10.1016/j.future.2020.10.040" @default.
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