← 研踪 PaperTrailarXiv:2601.21359 · v2
Graph-Free Root Cause Analysis
首次提交 2026-01-29 15:38
版本更新 2026-11-11 23:01
雷达观测 2026-11-13 14:16
摘要
Failures in complex systems demand rapid Root Cause Analysis (RCA) to prevent cascading damage. Graph-free RCA methods typically assume that the root cause has the highest anomaly score. This assumption fails when faults propagate, because a small deviation at the root cause can grow into a larger anomaly downstream. Our key observation is that a root cause deviates in both its internal properties (e.g., resource usage) and external properties (e.g., latency), whereas components affected by propagation deviate only in external properties. We propose CIE, a framework that ranks components by combining their internal and external anomaly scores. We formalize a class of component-based systems and state conditions under which CIE provably ranks the root cause first. The guarantee holds under arbitrary amplification for a conjunctive score, and under bounded amplification and a stronger root-cause condition for the default additive score. On 735 failures in nine datasets from three microservice systems, CIE achieves 68% Top-1 accuracy averaged over 11 fault types, a 21% relative improvement over the best of twelve baselines, while requiring 8 ms per diagnosis.
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v2 · 2026-11-11 23:01 · 最新观测版本
Graph-Free Root Cause Analysis
Failures in complex systems demand rapid Root Cause Analysis (RCA) to prevent cascading damage. Graph-free RCA methods typically assume that the root cause has the highest anomaly score. This assumption fails when faults propagate, because a small deviation at the root cause can grow into a larger anomaly downstream. Our key observation is that a root cause deviates in both its internal properties (e.g., resource usage) and external properties (e.g., latency), whereas components affected by propagation deviate only in external properties. We propose CIE, a framework that ranks components by combining their internal and external anomaly scores. We formalize a class of component-based systems and state conditions under which CIE provably ranks the root cause first. The guarantee holds under arbitrary amplification for a conjunctive score, and under bounded amplification and a stronger root-cause condition for the default additive score. On 735 failures in nine datasets from three microservice systems, CIE achieves 68% Top-1 accuracy averaged over 11 fault types, a 21% relative improvement over the best of twelve baselines, while requiring 8 ms per diagnosis.
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