Reinforcement Learning for Adaptive Resource Management in Cloud Software

Abstract

Cloud software systems operate under highly dynamic and unpredictable workloads, requiring efficient and adaptive resource management strategies to maintain performance, reliability, and cost efficiency. Traditional rule-based and heuristic resource allocation approaches often fail to respond optimally to rapid workload fluctuations and complex system interactions. This paper proposes a reinforcement learning-based adaptive resource management framework that enables cloud systems to autonomously learn optimal resource allocation policies through continuous interaction with the environment. By modeling cloud resource management as a sequential decision-making problem, the framework leverages reinforcement learning algorithms such as Q-learning, Deep Q-Networks (DQN), and policy-gradient methods to dynamically adjust computing resources including CPU, memory, and virtual machine instances. The proposed approach aims to optimize multiple objectives such as performance, cost, and service-level agreement (SLA) compliance. Experimental evaluation using simulated and real-world cloud workloads demonstrates that reinforcement learning significantly outperforms static and reactive baseline strategies in terms of resource utilization efficiency and response time stability. The results highlight the potential of reinforcement learning to enable intelligent, self-adaptive cloud resource management systems.