🤖 AI Summary
This work addresses the challenge of root cause localization in microservice architectures, where cascading anomalies are often obscured by downstream noise and weak source signals. To this end, the authors propose eIRWR, a method that leverages the service dependency graph within a personalized PageRank framework. By incorporating a transition matrix enhanced with power-law restart focusing, self-loops, and backward edges, and optimizing belief propagation through outer-loop iterations, eIRWR enables precise anomaly evidence propagation and accurate accumulation of root-source probabilities. Evaluated on large-scale Alibaba datasets (12K–25K nodes), eIRWR achieves an MRR of 0.75 under moderate observability—2.8× higher than the best baseline—and 0.94 under high observability, with per-inference latency below 25 ms, demonstrating strong potential for online deployment.
📝 Abstract
Root cause analysis (RCA) in microservice architectures needs to pinpoint the originating faulty service responsible for the cascading symptoms seen across hundreds or thousands of interdependent services. Graph-based random walk methods propagate anomaly evidence over the service dependency graph. However, existing anomaly-restart walks leave much of the localization signal unused: they restart from the raw anomaly vector, which is dominated by loud downstream victims rather than the quieter source. Through a controlled ablation, we first show that the "resilience damping" often applied to the transition matrix is mathematically equivalent to raising the restart probability; we therefore benchmark against a restart-tuned Personalized PageRank (PPR) rather than its default configuration. We then present Enhanced Iterative Random Walk with Restart (eIRWR), which (a) concentrates restart mass on the most suspicious nodes through power-law teleportation sharpening, (b) augments the transition matrix with self-loops and backward edges so that probability accumulates at cascade sources, and (c) refines its belief across an outer loop. On three large-scale topologies (12K-25K nodes) from the Alibaba Microservice Trace Dataset, eIRWR attains a Mean Reciprocal Rank (MRR) of 0.75 at moderate root-cause visibility, a 2.8 times improvement over the best aggregate-metric baseline and well above a restart-tuned PPR. At high visibility, it reaches MRR= 0.94, while running in under 25ms on graphs with 17,000 nodes, making it suitable for online deployment.