Anomaly Detection and Root Cause Analysis for Microservice Systems
A new thesis proposes methods to improve anomaly detection and root cause analysis in microservice systems, addressing limitations in current approaches. The research introduces frameworks like BARO for metric data, EventADL for event data, and TORAI which does not require a service call graph. Additionally, it delivers a benchmarking dataset and evaluation framework called RCAEval to facilitate fair comparison of future research in this domain. AI