STAR: Adaptive Spatial-Temporal Normalization for Unified Microservice Incident Management

Automated incident management is critical for microservice reliability. While recent unified frameworks leverage multimodal data for joint optimization, they unrealistically assume perfect data completeness. In practice, network fluctuations and agent failures frequently cause missing modalities. Existing approaches relying on static placeholders introduce imputation noise that masks anomalies and degrades performance. To address this, we propose ARMOR, a robust self-supervised framework designed for missing modality scenarios. ARMOR features: (i) a modality-specific asymmetric encoder that isolates distribution disparities among metrics, logs, and traces; and (ii) a missing-aware gated fusion mechanism utilizing learnable placeholders and dynamic bias compensation to prevent cross-modal interference from incomplete inputs. By employing self-supervised auto-regression with mask-guided reconstruction, ARMOR jointly optimizes anomaly detection (AD), failure triage (FT), and root cause localization (RCL). AD and RCL require no fault labels, while FT relies solely on failure-type annotations for the downstream classifier. Extensive experiments demonstrate that ARMOR achieves state-of-the-art performance under complete data conditions and maintains robust diagnostic accuracy even with severe modality loss.

Microservice systems are widely used to build cloud applications, yet their complexity makes failures inevitable, degrading user experience and causing economic loss. Automated anomaly detection and root cause analysis (RCA) are now active research areas, but existing techniques share five limitations. First, most treat anomaly detection and RCA separately, assuming anomalies are detected correctly, and falter when detection is imprecise due to noise or delay. Second, they focus on metrics, logs, and traces, leaving event data such as API calls and configuration changes underexplored. Third, many require a given service call graph and cannot diagnose without one. Fourth, the field lacks standardised datasets and evaluation frameworks, so methods are hard to compare fairly. Fifth, although causal inference-based RCA has become dominant, its effectiveness, efficiency, and robustness remain unclear. This thesis addresses these limitations through two groups of contributions. The first introduces methods that exploit observability data both independently and collectively. BARO is an end-to-end anomaly detection and RCA approach for metric data. EventADL is an end-to-end framework for event data. TORAI is a multimodal RCA framework that requires no service call graph. Extensive experiments on real microservice systems demonstrate their effectiveness and robustness. The second group delivers benchmarking datasets, an evaluation framework, and systematic evaluation efforts. RCAEval is a comprehensive benchmark providing ready-to-use datasets and reproducible baselines for future research. A systematic evaluation of existing RCA methods, especially causal inference-based approaches, offers insights that guide future directions. This thesis thereby advances automated anomaly detection and RCA for microservice failures, enabling future research on incident mitigation and remediation.

Anomaly detection and localization (ADL) is critical for maintaining reliability and availability in cloud systems. Recent ADL developments focus on metric and log data, leaving event data unexplored. To address this gap, we propose EventADL, the first open-box event-based ADL framework for cloud-based service systems. To motivate the design of our framework, we conduct a systematic analysis on 520 real-world incidents, and provide insights into how anomalies and their root causes manifest through event data. EventADL has three phases: offline training, online anomaly detection, and root cause localization. During the training phase, EventADL first learns Event Semantic Patterns (ESPs), which capture normal interactions between system entities using historical event data, and then learns Event Frequency Patterns (EFPs), which capture the normal frequency of known ESPs. In the online anomaly detection phase, any data in the event stream that deviates significantly from either pattern is identified as anomalous. For localization, EventADL constructs an Intervention Graph that models the relationships between recent system interactions and the detected anomalies for automatic root cause localization. The framework is designed to operate efficiently with unlabeled data and to produce interpretable anomalies with their corresponding root causes. Our evaluation on three real cloud service systems and two real-world incidents demonstrates that EventADL outperforms existing methods, achieving F1-scores of at least 90% for anomaly detection and 100% top-3 accuracy in root cause localization.