CARE: Context Aware Root Cause Identification Using Distributed Traces and Profiling Metrics

doi:10.1109/TSE.2025.3645143

CARE: Context Aware Root Cause Identification Using Distributed Traces and Profiling Metrics

2025-12-17

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Abdelwahab Hamou‐Lhadj

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James Miller

DOI：10.1109/TSE.2025.3645143

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Root cause localization in microservices is challenging due to intricate service dependencies and the high volume and heterogeneity of collected monitoring data, which add complexity to the analysis. Conventional methods often overlook nuanced propagation patterns and contextual interactions among services, and they are limited in leveraging multi-source observability data for comprehensive root cause identification. This study introduces CARE, a context-aware, spectrum-analysis-based approach that integrates multi-source observability data and employs network analysis to prioritize the contextual significance of components in propagating anomalies across individual services, service communities, and requests. CARE’s weighted spectrum analysis leverages these prioritized contexts to pinpoint underlying performance issues. Evaluations on 224 cases from the TrainTicket benchmark and a real-world Internet service provider’s production system demonstrate CARE’s substantial accuracy gains, with top-1 accuracy of 72%-89% and top-5 accuracy of 84%-99% for single root causes, outperforming baselines by 8%-41%. CARE also shows significant improvements in dual root cause identification, exceeding baseline performance by 18%-37%, all while maintaining efficient resource usage, establishing CARE as a robust and resource-effective solution for root cause localization in complex microservice environments.

Keywords:

Distributed traces

network analysis

spectrum analysis

root cause analysis

AIOps

CARE: Context Aware Root Cause Identification Using Distributed Traces and Profiling Metrics

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