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.

翻译：云计算的运行依赖于大规模网络，这些网络本质上是复杂系统。本文提出一种新颖的云网络事件根本原因分析方法，利用基于图的因果发现技术。我们的方法通过引入时空分组策略和自动化本体来降低问题维度，从而解决了基于规则自动化的局限性。我们利用双变量格兰杰因果关系和条件独立性检验，从二元时间序列数据中构建因果图。在推理方面，我们引入了一种概率方法，该方法将边特定的条件概率分配为时间延迟的函数，从而通过因果图遍历实现可解释的、时间感知的根因评分。我们使用来自一家主要云提供商的35个生产事件的标记数据集对系统进行了评估。该模型在85.7%的事件中成功召回正确的根本原因，并在74.3%的事件中产生精确匹配。在生产环境中，已部署的系统已用于800多个真实事件，网络工程师给出了积极的定性反馈。这些结果突显了在动态且大规模运营环境中采用数据驱动的因果方法进行根本原因分析的实用性。