用因果图分析云网络故障，精准定位根因。
Graphical Causal Reasoning for Root Cause Analysis in Cloud Networks
- 基于时间序列构建因果图，通过时滞概率评分定位根因。
- 在35个真实故障中召回率85.7%，精确匹配率达74.3%。
- 已在超800次生产故障中应用，工程师反馈积极。
云计算依赖大规模复杂网络，本文提出一种新型根因分析（RCA）方法，利用基于图的因果发现技术解决规则化自动化方法的局限性。通过引入时空分组策略和自动化本体，降低问题维度；从二值时间序列数据构建因果图，采用双变量格兰杰因果与条件独立性检验；推理阶段提出概率方法，以时滞为函数计算边级条件概率，实现可解释、时序感知的根因评分。在某大型云服务商提供的35个标注生产故障数据集上评估，模型成功召回正确根因达85.7%，实现精确匹配74.3%。系统已部署于超过800次实际故障场景，获网络工程师积极反馈。结果表明，数据驱动的因果方法在动态大规模运维环境中具备实用性。
原文摘要 · Abstract (English)
Cloud-computing relies on large-scale networks which are inherently complex systems. In this paper, we present a novel approach to root cause analysis (RCA) of cloud network incidents, leveraging graph-based causal discovery techniques. Our method addresses the limitations of rule-based automation by introducing a spatiotemporal grouping strategy and an automation ontology to reduce the dimensionality of the problem. We construct a causal graph from binary time series data using bivariate Granger causality and conditional independence tests. For inference, we introduce a probabilistic method that assigns edge-specific conditional probabilities as a function of time lag, allowing for interpretable, time-aware root cause scoring via causal graph traversal. We evaluated the system using a labeled dataset of 35 production incidents from a major cloud provider. The model successfully recalled the correct root cause in 85.7% of incidents and produced an exact match in 74.3%. In production, the deployed system has been used in over 800 real-world incidents, with positive qualitative feedback from network engineers. These results highlight the practicality of a data-driven, causal approach to RCA in dynamic and large-scale operational environments.
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