Robust and Lightweight Root Cause Analysis for Microservice Metrics

In microservice management, engineers analyze numerous heterogeneous performance metrics, including latency, CPU, and memory usage, to resolve incidents swiftly. Consequently, a metric-based root cause analysis (RCA) must demonstrate interpretability, accuracy, and efficiency. However, recent causal-discovery-based RCAs have been identified as having challenges in terms of accuracy and efficiency. This paper highlights the issues arising from the heterogeneity of metrics and attempts to improve causal-aware RCA while leveraging the metadata associated with the metrics. We adopt the validated concept from existing RCA methods that metrics showing greater deviations during a failure are likely the root causes. The proposed method quantifies metric deviations comparable among heterogeneous metrics and searches for propagation trees that best explain the observed deviations. Experimental results on microservice-based system benchmark datasets demonstrate that the proposed method outperforms existing RCA methods in terms of accuracy and efficiency.