无依赖图时，精准定位故障根源，速度超快。
Graph-Free Root Cause Analysis
- 基于组件系统建模，不依赖依赖图进行故障溯源
- 在9个真实数据集上达68%的根因定位准确率
- 仅需8毫秒/次诊断，适合大规模系统实时分析
复杂系统故障需快速定位根因以防止连锁失效。现有无需依赖图的方法通常假设异常分数最高的组件即为根因，但在故障传播场景下，根因的小延迟会累积成下游显著异常，导致误判。本文提出PRISM框架，在无依赖图条件下实现高效且有理论保障的根因分析。该方法适用于一类组件化系统，并在9个真实数据集共735次故障上验证：相比最佳基线，其Top-1准确率提升258%，达到68%，每次诊断仅需8毫秒。
原文摘要 · Abstract (English)
Failures in complex systems demand rapid Root Cause Analysis (RCA) to prevent cascading damage. Existing RCA methods that operate without dependency graph typically assume that the root cause having the highest anomaly score. This assumption fails when faults propagate, as a small delay at the root cause can accumulate into a much larger anomaly downstream. In this paper, we propose PRISM, a simple and efficient framework for RCA when the dependency graph is absent. We formulate a class of component-based systems under which PRISM performs RCA with theoretical guarantees. On 735 failures across 9 real-world datasets, PRISM achieves 68% Top-1 accuracy, a 258% improvement over the best baseline, while requiring only 8ms per diagnosis.
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