Reinforcement Learning for Adaptive Resource Management in Cloud Systems - Muddam, Rajender Reddy (International Journal of AI, BigData, Computational and Management Studies, 2026)
Cloud software systems operate under unpredictable and constantly changing workloads. Static provisioning and rule-based auto-scaling strategies often respond too late to performance issues or waste resources during low-demand periods. This creates a tradeoff between cost efficiency and service reliability that traditional approaches struggle to balance. This paper presents a reinforcement learning based framework for adaptive cloud resource management. The system learns how to allocate computing resources by interacting with the cloud environment and observing the long-term outcomes of its decisions. Cloud management is modeled as a sequential decision process where the learning agent balances performance, cost, and service-level agreement compliance. We evaluate the proposed approach using simulated cloud workloads and compare it with threshold-based and reactive scaling strategies. Results show improved resource utilization, reduced SLA violations, and smoother adaptation to workload changes. The findings suggest that reinforcement learning offers a practical foundation for building self-adaptive cloud systems that improve over time without manual rule tuning.