Dynamic Resource Allocation in Cloud Environments Using Reinforcement Learning

Abstract

In cloud computing environments, efficient resource management is crucial for optimizing performance, minimizing costs, and ensuring scalability. Traditional resource allocation techniques often face challenges in dealing with dynamic workloads, varying demands, and resource constraints. This paper explores the application of Reinforcement Learning (RL) for dynamic resource allocation in cloud environments. We present a detailed analysis of RL-based techniques for allocating resources efficiently, addressing both single and multi-agent scenarios. The study highlights the strengths of RL in adapting to changing conditions, providing insights into its potential to revolutionize cloud resource management. A comparison between conventional algorithms and RL-based approaches is provided, illustrating RL’s effectiveness in improving resource allocation accuracy, cost reduction, and scalability. The proposed approach is validated through simulations and real-world case studies.