Leveraging Reinforcement Learning and Genetic Algorithms for Enhanced Cloud Infrastructure Optimization

Abstract

This paper explores the synthesis of reinforcement learning (RL) and genetic algorithms (GAs) to optimize cloud infrastructure management, addressing the growing complexity in resource allocation, energy efficiency, and cost-effectiveness. The research develops a hybrid framework that combines the adaptive learning capabilities of RL with the global search proficiency of GAs, aiming to enhance decision-making processes in dynamic cloud environments. The proposed approach iteratively refines resource allocation strategies by utilizing RL to learn from real-time feedback and environment interactions, while GAs optimize the policy space by evolving a population of potential solutions. Experimental results, conducted on a simulated cloud platform with varying workloads and resource demands, demonstrate that the hybrid method surpasses traditional techniques in minimizing operational costs and energy consumption, achieving up to a 20% improvement in efficiency. The scalability of the system is further validated across multi-tenant scenarios, where the adaptive nature of RL enables rapid convergence even with fluctuating user demands. Additionally, a comparative analysis with state-of-the-art optimization algorithms highlights the hybrid approach’s robustness and its ability to adapt to complex, non-stationary environments. This research provides compelling evidence for the integration of RL and GAs in cloud infrastructure, proposing a novel pathway for achieving sustainable and cost-effective cloud services in an increasingly digital world.