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• W3157457932 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 Micro-Diag, 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%." @default.
• W3157457932 created "2021-05-10" @default.
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• W3157457932 date "2021-05-01" @default.
• W3157457932 modified "2023-10-02" @default.
• W3157457932 title "MicroDiag: Fine-grained Performance Diagnosis for Microservice Systems" @default.
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• W3157457932 doi "https://doi.org/10.1109/cloudintelligence52565.2021.00015" @default.
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