AI-driven cloud optimization: Leveraging machine learning for dynamic resource allocation

This research paper explores the application of artificial intelligence (AI) and machine learning (ML) techniques in optimizing cloud resource allocation. The study investigates how AI-driven approaches can enhance the efficiency and effectiveness of cloud computing systems through dynamic resource allocation. We present a comprehensive review of existing methodologies, propose novel algorithms, and conduct extensive experiments to validate the effectiveness of our approach. The results demonstrate significant improvements in resource utilization, cost reduction, and overall system performance compared to traditional static allocation methods.