Harnessing Multi-Agent Reinforcement Learning for Dynamic Resource Allocation in Enterprise Cloud Environments

Abstract

Enterprise cloud environments support diverse applications and workloads that require efficient allocation of computing resources, including central processing units, memory, storage, and network bandwidth. Traditional resource allocation techniques often struggle to respond effectively to rapidly changing workloads, unpredictable demand, and competing application requirements. Multi-Agent Reinforcement Learning (MARL) offers a promising approach to addressing these challenges by enabling multiple intelligent agents to collaboratively learn optimal resource allocation strategies through continuous interaction with dynamic cloud environments. This study explores the application of MARL for dynamic resource allocation in enterprise cloud infrastructures, focusing on improving resource utilization, minimizing operational costs, reducing service response times, and maintaining quality of service. The proposed approach considers a distributed multi-agent architecture in which individual agents manage specific cloud resources or virtualized environments while coordinating their decisions to achieve overall system efficiency. The research methodology incorporates workload simulation, reinforcement learning algorithms, performance evaluation, and comparative analysis against conventional resource management techniques. Key performance indicators include resource utilization, task completion time, energy consumption, allocation efficiency, and service-level agreement compliance. The study aims to demonstrate how collaborative learning and adaptive decision-making can improve cloud resource management while supporting scalability, flexibility, and operational reliability in complex enterprise computing environments.