Self-Healing Systems: Reinforcement Learning For Cloud Resilience

Abstract

The exponential growth of cloud computing infrastructure has posed unprecedented challenges to conventional incident management methods, which, ever more frequently, fail to cope with the dynamic complexity of contemporary distributed systems. Reinforcement learning is an innovation in tackling autonomous cloud remediation, allowing self-healing infrastructures to learn from disruption events and improve their resilience capacities ever further. Deep Q-Networks and policy gradient algorithms like Proximal Policy Optimization exhibit superior performance in discrete and continuous action space modeling for cloud remediation use cases, while multi-agent reinforcement learning architectures tackle distributed systems of the cloud via synchronized decision-making among independent agents controlling different infrastructure domains. Hierarchical reinforcement learning algorithms break down complex remediation processes into tractable sub-policies, greatly enhancing learning efficiency and system explainability. Production deployments show dramatic gains in Mean Time to Recovery and system availability, with agents powered by RL effectively handling enormous container orchestration and consistently delivering high service levels through predictive recovery of failures. Autonomous remediation systems' deployment, however, introduces key ethical issues around accountability, transparency, and human control, specifically the "black box" characteristics of deep RL policies and concerns over runaway automation. Future paradigms unify meta-learning and continuous learning domains to support fast adaptation without catastrophic forgetting, and digital twin representations support safe policy exploration and federated learning methods supporting knowledge sharing across organizational boundaries while maintaining a competitive edge.