New Methods for Detecting and Diagnosing Failures in Microservice Cloud Systems
Researchers have developed new automated techniques for detecting anomalies and identifying root causes of failures in microservice-based cloud applications. The work addresses five key limitations in existing approaches, including the separation of detection and diagnosis, limited use of event data, and lack of standardized evaluation frameworks. These advances could help reduce downtime and economic losses from system failures in cloud infrastructure.
A new research thesis presents multiple contributions to automated anomaly detection and root cause analysis (RCA) in microservice systems, which are complex distributed architectures used to build modern cloud applications. The work identifies and addresses five significant limitations in existing techniques: most treat anomaly detection and RCA as separate problems, focus narrowly on metrics and logs while ignoring event data, require pre-existing service call graphs, lack standardized datasets for fair comparison, and leave questions about the effectiveness of causal inference-based approaches. The researchers introduce three main methods—BARO for metric-based analysis, EventADL for event data, and TORAI for multimodal analysis without requiring service graphs—along with RCAEval, a comprehensive benchmarking framework with datasets and baselines. Extensive experiments on real microservice systems demonstrate the effectiveness and robustness of these approaches, with the systematic evaluation of existing RCA methods providing guidance for future research directions.
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- arXiv cs.AICenter
Anomaly Detection and Root Cause Analysis for Microservice Systems
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