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
RANK_REASON The cluster contains a single academic paper detailing new methods and datasets for a specific research area. [lever_c_demoted from research: ic=1 ai=0.7]
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