Automated Root Cause Analysis and Mitigation in Large-Scale Cloud Infrastructure Using Dynamic Bayesian Networks and Reinforcement Learning

**Abstract:** This paper proposes a novel framework for automated root cause analysis (RCA) and mitigation in complex, large-scale cloud infrastructure. Leveraging Dynamic Bayesian Networks (DBNs) to model the cascading failure dependencies and Reinforcement Learning (RL) agents for proactive mitigation, our system – the Dynamic Cloud Resilience Optimizer (DCRO) – achieves significantly improved fault detection and resolution compared to traditional monitoring and alerting systems. The DCRO provides a real-time, adaptive solution capable of identifying intricate root causes obscured by numerous surface-level symptoms, allowing for preemptive interventions and minimizing service disruption, potentially reducing incident resolution time by up to 60% and preventing revenue loss in highly available cloud environments.