PFMR: ALLOCATING SUPPORT BY LOCAL EFFECT FOR TRACE-BASED ROOT CAUSE LOCALIZATION

A single microservice failure can produce anomalies across a request path, making it difficult to decide which service to inspect first. Statistical support for a change and the magnitude of local deterioration carry different diagnostic information. We propose PFMR, a trace-based method that uses positive local effect to allocate two-window rank support across services. It updates a change reference with effect-weighted support, pooling the two signals within each service. Under additive execution, we derive a condition for separating local deterioration from propagated anomalies; a finite support ceiling ensures that weak local effects contribute vanishingly little at fixed sample counts. PFMR achieves 78.13% and 87.92% service-level Top-1 accuracy on FSE1422 and RCAEval240. At a fixed total support contribution, it improves Top-1 by 5.70 and 8.33 percentage points over a common coefficient and by 3.38 and 3.33 points over normalized additive fusion. Prediction-defined groups show the largest gains when the reference and support prefer different services and the support-preferred service has the larger local effect.