AI 中文总结
该研究针对微服务根因分析的现有随机游走算法定位信号利用不足的问题，提出eIRWR算法，通过三项核心设计提升性能，在阿里巴巴微服务追踪数据集上取得显著优于基准的效果，且运行高效适合在线部署。
AI 中文摘要
微服务架构中的根因分析（RCA）需要精准定位导致成百上千个相互依赖服务级联故障症状的源头故障服务。基于图的随机游走方法会在服务依赖图上传播异常证据，但现有的异常重启游走算法未充分利用大部分定位信号：它们从原始异常向量重启，而该向量由下游受影响更严重的节点主导，而非更安静的源头。通过受控消融实验，我们首先证明，通常应用于转移矩阵的“ resilience damping（ resilience阻尼）”在数学上等价于提高重启概率；因此我们采用重启调优后的个性化 PageRank（PPR）作为基准，而非其默认配置。随后我们提出增强型迭代随机游走重启算法（eIRWR），该算法包含三个核心设计：（a）通过幂律 teleportation（ teleportation teleport）锐化将重启质量集中在最可疑节点上；（b）为转移矩阵添加自环和反向边，使概率在级联源头累积；（c）通过外层循环细化其置信度。在来自阿里巴巴微服务追踪数据集的三个大规模拓扑（1.2万至2.5万个节点）上，eIRWR在中等根因可见度下达到0.75的平均倒数排名（MRR），较最佳聚合指标基准提升2.8倍，且显著高于重启调优后的PPR；在高可见度下，其MRR达到0.94，同时在1.7万个节点的图上运行时间不足25毫秒，适合在线部署。
英文摘要
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.
