New Methods for Detecting and Diagnosing Failures in Microservice Cloud Systems
A PhD thesis submitted to RMIT University introduces a suite of methods and benchmarking tools to improve automated anomaly detection and root cause analysis (RCA) for cloud-based microservice systems. The work addresses five identified gaps in existing research, including the separation of detection and analysis pipelines, underuse of event data, and lack of standardized evaluation frameworks. The contributions aim to make cloud failure diagnosis more reliable and comparable across the research community.
Researcher Luan Pham's PhD thesis, submitted to RMIT University and posted as a preprint on arXiv, presents new approaches to detecting and diagnosing failures in microservice-based cloud applications. The thesis identifies five key limitations in current methods: treating anomaly detection and RCA as separate problems, neglecting event data such as API calls and configuration changes, relying on pre-existing service call graphs, lacking standardized datasets, and having unclear effectiveness of causal inference-based RCA. To address these, the thesis introduces three systems—BARO for end-to-end metric-based detection and RCA, EventADL for event data, and TORAI, a multimodal framework that operates without a service call graph. A benchmarking suite called RCAEval is also introduced, providing reproducible datasets and baselines to enable fair comparison of future methods. Experiments conducted on real microservice systems are reported to demonstrate the effectiveness and robustness of the proposed approaches.
What's missing
As a preprint PhD thesis, the work has not yet undergone formal peer review, making independent assessment of claimed improvements difficult at this stage.
What different sources said
- arXiv cs.AICenter
Anomaly Detection and Root Cause Analysis for Microservice Systems
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