Hyperbolic Spatio-Temporal Graph Learning With Agent Reasoning for Root Cause Localization in Cloud-Edge Microservices

Abstract

Root cause localization is critical for ensuring service reliability in cloud-edge collaborative microservice systems. In practical scenarios, multiple microservice systems are often hybrid-deployed on shared infrastructure, which poses three challenges for existing methods. First, concurrent systems generate substantial metric noise that interferes with anomaly detection. Second, the hierarchical dependencies spanning cloud, edge, and terminal layers cannot be accurately represented in Euclidean space. Third, gateway services that aggregate traffic from multiple systems exhibit amplified anomaly signals, leading to systematic false alarms. To address these issues, we propose confidence gated agent root cause localization (CGARCL), a framework that integrates hyperbolic geometry with confidence gated agent reasoning. CGARCL consists of three components. The direction constrained budgeted anomaly detection method incorporates baseline robust scoring and temporal continuity constraints to extract high-quality candidate anomalous nodes from noisy metrics. The hyperbolic constrained spatio-temporal graph attention network employs Poincar’e ball mapping and center-based topology aggregation to accurately encode hierarchical service dependencies and generate initial root cause rankings. The confidence gated reranking agent is activated when the score gap between the top two candidates is small or the top-ranked node matches a victim-prone pattern. It then performs structured prompt reasoning to suppress false alarms and produce a refined ranking. Experiments on three cloud-edge collaborative microservices datasets demonstrate that CGARCL achieves ACC@1 of 62.1%, 70.6%, and 73.4%, outperforming the second best approach by 19.1%, 11.4%, and 9.8%.