Reinforcement Learning Techniques for Autonomous Cloud Optimization and Adaptive Resource Management
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V6I3P119Keywords:
Reinforcement Learning, Cloud Computing, Autonomous Optimization, Resource Management, Deep Reinforcement Learning, Autoscaling, Load Balancing, Adaptive Scheduling, Markov Decision ProcessAbstract
Cloud computing has emerged as the backbone of modern digital infrastructure, supporting scalable applications ranging from enterprise systems to artificial intelligence-driven services. However, the rapid growth of cloud-native workloads has exposed critical limitations in traditional static and rule-based resource management techniques. These limitations include inefficient resource utilization, high latency, increased operational costs, and poor adaptation to dynamic workloads. To address these challenges, this paper explores the application of Reinforcement Learning (RL) techniques for autonomous cloud optimization and adaptive resource management. Reinforcement Learning offers a paradigm where intelligent agents learn optimal resource allocation strategies through continuous interaction with the cloud environment. Unlike traditional heuristic-based approaches, RL-based models dynamically adapt policies in real time based on reward feedback, leading to superior efficiency and automation. This paper presents a comprehensive analysis of RL algorithms such as Q-Learning, Deep Q Networks (DQN), Policy Gradient Methods, Proximal Policy Optimization (PPO), and Actor-Critic architectures in the context of cloud resource optimization. The proposed framework integrates workload prediction, autoscaling policies, virtual machine (VM) placement, container orchestration, and energy-efficient scheduling in a unified RL-based control system. The paper develops a mathematical formulation of the cloud optimization problem as a Markov Decision Process (MDP), defines state-action-reward structures, and provides training and deployment strategies for real-world cloud environments. Experimental evaluations were conducted in simulated and real-world hybrid cloud environments using synthetic and real workload traces. Results demonstrate significant improvements in resource utilization (up to 32%), reduction in operational cost (up to 28%), and latency improvement (up to 25%) when compared to conventional threshold-based and static autoscaling methods. This work contributes a scalable and autonomous cloud management architecture, detailed performance analysis, and implementation guidelines for practical deployment. The findings confirm that Reinforcement Learning is a highly effective approach for achieving intelligent, self-optimizing cloud infrastructures in complex and dynamic operational conditions
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