How Time-Series Decomposition Boosts Microservice Root Cause Localization to 84% Accuracy

This paper presents StudRank, a microservice root‑cause localization method that decomposes call‑chain traces into time‑series, detects anomalies, builds an abnormal propagation graph, and applies a personalized PageRank random‑walk algorithm, achieving 84% top‑1 accuracy and a 97.6% improvement over MicroRCA on public AIOps data.

MicroservicesStudRankaiops
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How Time-Series Decomposition Boosts Microservice Root Cause Localization to 84% Accuracy