Self-Optimizing Infrastructure through Multi-Agent Reinforcement Learning for Predictive Cloud Resource Management

Abstract

The increasing complexity of cloud computing environments has created a demand for intelligent resource management techniques that can dynamically optimize infrastructure performance, reduce operational costs, and maintain service-level agreements. Traditional resource allocation approaches often rely on static thresholds and reactive scaling mechanisms, which may not adequately respond to fluctuating workloads and unpredictable resource demands. This study explores self-optimizing cloud infrastructure through multi-agent reinforcement learning (MARL) for predictive cloud resource management. The proposed approach conceptualizes cloud infrastructure as a distributed environment in which autonomous agents collaborate to forecast workload variations, allocate computational resources, optimize virtual machine placement, and regulate energy consumption. Each agent learns an effective resource management policy through interactions with the environment, while coordination mechanisms support decisions across interconnected infrastructure components. The research methodology integrates workload prediction, reinforcement learning, resource allocation, and performance evaluation using relevant operational metrics, including response time, resource utilization, energy consumption, provisioning cost, and service-level agreement violations. A comparative evaluation against conventional threshold-based and single-agent reinforcement learning approaches is proposed to assess the potential benefits of collaborative intelligence. The study aims to establish a scalable framework for proactive, adaptive, and autonomous cloud management. The expected outcomes include improved resource efficiency, reduced provisioning overhead, enhanced workload responsiveness, and greater infrastructure resilience under dynamic operating conditions.