AI-Enabled Predictive Analytics for Cloud Resource Management: A Reinforcement Learning-Based Approach for Cost and Performance Optimization

Abstract

Cloud computing has revolutionized the way organizations manage their IT infrastructure, offering scalable and flexible resources on demand. However, optimizing cloud resource management to balance cost and performance remains a significant challenge. This paper presents an AI-enabled predictive analytics framework that leverages reinforcement learning (RL) to dynamically allocate and manage cloud resources. The proposed approach, named CloudRL, integrates predictive analytics to forecast resource demand and RL to make real-time decisions that optimize both cost and performance. We evaluate CloudRL using a comprehensive set of experiments and simulations, demonstrating its effectiveness in reducing operational costs while maintaining high performance levels. The results show that CloudRL outperforms traditional resource management strategies in terms of cost savings and resource utilization efficiency