Graphical Causal Reasoning for Root Cause Analysis in Cloud Networks

Problem
The paper addresses the limitations of existing rule-based automation methods for root cause analysis (RCA) in cloud networks, which are complex and dynamic systems. Current approaches often struggle with high dimensionality and lack interpretability. This work is particularly relevant as it is a preprint and has not undergone peer review, indicating that the findings should be interpreted with caution.

Method
The authors propose a novel RCA framework that utilizes graph-based causal discovery techniques. They construct a causal graph from binary time series data by employing bivariate Granger causality and conditional independence tests. A key innovation is the introduction of a spatiotemporal grouping strategy and an automation ontology, which effectively reduces the dimensionality of the problem. For inference, the authors develop a probabilistic method that assigns edge-specific conditional probabilities based on time lag, enabling interpretable and time-aware root cause scoring through causal graph traversal. The model was trained and evaluated on a labeled dataset comprising 35 production incidents from a major cloud provider.

Results
The proposed method achieved a recall of 85.7% for identifying the correct root cause and an exact match rate of 74.3% on the labeled dataset. In practical applications, the deployed system has been utilized in over 800 real-world incidents, receiving positive qualitative feedback from network engineers. These results demonstrate a significant improvement in RCA accuracy compared to traditional methods, although specific baseline comparisons are not detailed in the paper.

Limitations
The authors acknowledge that their approach may be limited by the quality and representativeness of the training data, as it is based on a relatively small dataset of 35 incidents. Additionally, the reliance on binary time series data may restrict the model’s applicability to other types of data or more complex causal relationships. The paper does not discuss potential scalability issues or the computational overhead associated with causal graph construction and traversal in larger networks.

Why it matters
This work has significant implications for enhancing incident response strategies in cloud computing environments, where rapid and accurate RCA is critical for maintaining service reliability. By leveraging causal reasoning, the proposed method offers a more interpretable and data-driven approach to RCA, which could inform future developments in automated network management systems. The findings contribute to the growing body of literature on causal inference in machine learning, as published in arXiv cs.LG.