Reinforcement Learning-Based Fault Recovery in Dynamic Distributed Systems: A Simulation Approach

Dynamic distributed systems face frequent and unpredictable failures due to changing workloads, node volatility, and heterogeneous environments. Traditional fault tolerance mechanisms, while effective, often rely on static policies or reactive strategies that fail to adapt in real-time. This paper presents a reinforcement learning (RL)-based framework for fault recovery that learns optimal task migration and recovery strategies through interaction with the system environment. We simulate a dynamic distributed environment where a Deep Q-Learning agent observes system metrics such as CPU usage, memory, and latency, and selects recovery actions like task reassignment or node reboot. Our experiments demonstrate that the RL agent improves system resilience over time, achieving higher task completion rates and reduced recovery latency compared to baseline static policies. This work highlights the potential of RL to create adaptive, self-improving fault tolerance strategies suitable for modern distributed systems.