MicroMCM: Fine-grained Root Cause Localization for Microservice Systems Based on Multiple Causal Inference Methods

Le résumé fourni par la source

Microservice architecture has become a prevalent approach for developing large-scale applications due to its scalability, flexibility, and agility. However, the large-scale deployment and frequent updates of microservices pose challenges for operational personnel in diagnosing performance issues. To address this, we propose MicroMCM, a framework that enables fine-grained, automated, and real-time root cause localization. MicroMCM dynamically selects different causal inference (CI) methods based on diverse anomaly patterns to construct causal graphs and utilizes root cause inference techniques to identity the root cause metrics. We conduct experiments for both coarse-grained and fine-grained root cause localization to evaluate the performance of MicroMCM. The results demonstrate that MicroMCM outperforms baseline methods, exhibiting superior localization capabilities.

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