Graphical Causal Reasoning for Root Cause Analysis in Cloud Networks

• URL: http://arxiv.org/abs/2606.13532v1
• Date: Thu, 11 Jun 2026 16:20:51 GMT
• Title: Graphical Causal Reasoning for Root Cause Analysis in Cloud Networks
• Abstract summary: Cloud computing relies on large-scale networks which are inherently complex systems.<n>We present a novel approach to root cause analysis (RCA) of cloud network incidents, leveraging graph-based causal discovery techniques.
• Score: 6.527926874739627
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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