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MicroDiag: Fine-grained Performance Diagnosis for Microservice Systems

Abstract : Microservice architecture has emerged as a popular pattern for developing large-scale applications for its benefits of flexibility, scalability, and agility. However, the large number of services and complex dependencies make it difficult and time-consuming to diagnose performance issues. We propose MicroDiag, an automated system to localize root causes of performance issues in microservice systems at a fine granularity, including not only locating the faulty component but also discovering detailed information for its abnormality. MicroDiag constructs a component dependency graph and performs causal inference on diverse anomaly symptoms to derive a metrics causality graph, which is used to infer root causes. Our experimental evaluation on a microservice benchmark running in a Kubernetes cluster shows that MicroDiag localizes root causes well, with 97% precision of the top 3 most likely root causes, outperforming state-of-the-art methods by at least 31.1%.
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Submitted on : Tuesday, March 2, 2021 - 9:51:22 AM
Last modification on : Wednesday, March 3, 2021 - 3:07:20 AM
Long-term archiving on: : Monday, May 31, 2021 - 6:21:13 PM


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  • HAL Id : hal-03155797, version 1



Li Wu, Johan Tordsson, Jasmin Bogatinovski, Erik Elmroth, Odej Kao. MicroDiag: Fine-grained Performance Diagnosis for Microservice Systems. ICSE21 Workshop on Cloud Intelligence, May 2021, Madrid, Spain. ⟨hal-03155797⟩



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