AUTONOMOUS ORCHESTRATION OF VIRTUALIZED RESOURCES IN CLOUD ENVIRONMENTS USING REINFORCEMENT LEARNING AND DISTRIBUTED CONTROL MECHANISMS

Abstract

Cloud computing environments require intelligent orchestration of virtualized resources to optimize performance, minimize costs, and ensure scalability. Traditional resource allocation mechanisms often struggle with dynamic workloads and unpredictable demands. This paper explores the use of reinforcement learning (RL) and distributed control mechanisms to enhance autonomous orchestration in cloud environments. We review prior work on cloud resource management, propose a novel RL-based framework for dynamic allocation, and evaluate its effectiveness through simulations. Our results demonstrate significant improvements in efficiency, cost reduction, and response time over conventional methods.