Enterprise Reinforcement Learning for Dynamic Resource Optimization in Sustainable High-Performance Multi-Cloud Computing

Abstract

The rapid expansion of enterprise workloads across multi-cloud and high-performance computing environments has created significant challenges in resource allocation, energy efficiency, cost management, and operational sustainability. Conventional resource-management strategies generally depend on static provisioning, threshold-based scaling, or manually configured scheduling policies, which may not respond effectively to rapidly changing workload characteristics and heterogeneous cloud infrastructures. This research proposes an Enterprise Reinforcement Learning (ERL) framework for dynamic resource optimization in sustainable high-performance multi-cloud computing. The proposed framework uses reinforcement learning agents to continuously observe workload demand, resource utilization, application performance, energy consumption, network conditions, and cloud pricing information before selecting resource-management actions. The architecture integrates multi-cloud orchestration, workload prediction, intelligent scheduling, elastic scaling, energy-aware placement, and cost optimization into a unified decision-making framework. A multi-objective reward function balances performance, resource utilization, operational cost, energy consumption, carbon emissions, and service-level agreement requirements. The methodology evaluates reinforcement learning approaches against conventional scheduling and optimization techniques using metrics including throughput, latency, resource utilization, energy efficiency, carbon intensity, cost, SLA violations, and workload completion time. The framework also incorporates safety constraints and policy-based controls to prevent reinforcement learning agents from making harmful resource decisions. By continuously learning from changing cloud conditions, ERL can support adaptive and sustainable resource management across heterogeneous cloud providers. The research establishes a foundation for intelligent multi-cloud orchestration that simultaneously addresses enterprise performance requirements and environmental sustainability.