Adaptive Resource Allocation Using Reinforcement Learning for Performance and Cost Optimization

Abstract

Contemporary cloud computing systems encounter substantial difficulties in resource management as computing loads shift continuously and user requirements vary in unpredictable patterns. Conventional resource assignment techniques that rely on static policies struggle to adapt to these changing operational circumstances, leading to inefficient utilization of computing capacity and elevated expenses for business entities. This article investigates how machine learning techniques, particularly reinforcement learning methods, can improve resource management by allowing systems to automatically adjust resource distribution based on changing conditions. The article examines how resource allocation methods have developed over time, from basic rule-based systems to intelligent systems that can learn from experience. The text analyzes practical implementations across different industries and evaluates how these systems perform in real-world situations. The focus centers on understanding how organizations can balance performance requirements with cost considerations when deploying these intelligent resource management solutions. Findings reveal that machine learning-based resource allocation systems can significantly improve operational efficiency compared to traditional static methods. These systems demonstrate particular value in handling unpredictable workload patterns and complex multi-application environments where manual resource management becomes impractical. Results indicate that organizations implementing these adaptive methods can achieve substantial improvements in both system performance and cost effectiveness. This article contributes to understanding how intelligent resource management systems can be successfully integrated into existing infrastructure while addressing practical implementation challenges. Through evaluation of industry applications and performance outcomes, this text provides insights into the potential benefits of reinforcement learning methods for resource optimization in contemporary computing environments.