CARE: Context Aware Root Cause Identification Using Distributed Traces and Profiling Metrics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it