Causal Graph-Enhanced Large Language Models for Automated Fault Diagnosis and Intelligent Operation and Maintenance in Distributed Computing Systems

Modern distributed computing systems face increasingly complex architectural evolution and potentially costly failures, calling for efficient and robust automated diagnosis to ensure the stability of large-scale data processing. Existing data-driven approaches are constrained by scarce labeled data and black-box behaviors, while expert-based knowledge-driven solutions suffer from high construction costs and insufficient coverage of dynamic scenarios, especially when domain expertise is limited. This work proposes a fault diagnosis framework that integrates a unified causal graph (UCG) with large language models (LLMs), leveraging a dual knowledge-driven and data-driven mechanism to construct causal graph representations and dynamically generate structured diagnostic reasoning chains-of-thought based on system state awareness. Here, “causal” is used in a restricted sense, combining knowledge-driven dependencies with data-driven statistical regularities. Experimental results indicate that, using GPT-4o as an example, this study achieves accurate fault identification across the eight evaluated fault scenarios within the controlled evaluation scope of this study. Labeled instances are partitioned using stratified sampling into 80% for training and 20% for held-out evaluation; the procedure is repeated five times with independent train–test partitions, and reported matching rates are averaged across these runs. Compared with baselines that rely solely on fault information or on symptom information, the fault matching rate improves by 41.4% and 33.5%, respectively. By tightly coupling structured causal logic with generative artificial intelligence, the approach significantly enhances the interpretability and reliability of the diagnostic process and provides high-value, expert-level support for intelligent operations and maintenance (O&M) in distributed computing systems.