Predictive and Reinforcement Learning-Based Framework for Cloud Resource Optimization

The article presents a new optimization framework for dynamically managing resources in a cloud environment that combines predictive analytics and RL to optimally manage operational cost, system performance, and compliance with SLAs in multi-tenant cloud settings. Our framework comprises a predictive tier featuring resource demand forecasting based on regression models and an optimization tier that employs real-time resource allocation using Deep Q-Networks (DQN). Experiments conducted with real-world data from Microsoft Azure and MIT’s Supercloud have shown that the framework reduces over-provisioning and SLA violations while improving cost-efficiency. The automated dynamic resource allocation strategy proposed in this study outperforms traditional static allocation methods by reducing resource wastage and enhancing SLA compliance, demonstrating the viability of the approach in sophisticated multi-tenant cloud environments. This blended approach improves the efficiency of resource utilization while maintaining flexible and economical resource control. The findings accelerate the integration of AI-powered models, such as predictive analytics and reinforcement learning, with cloud resource management in response to the evolving challenges of cloud infrastructure complexity. We show that the proposed framework addresses the problems of achieving high-performance, scalable, and cost-effective optimization of cloud resources