THE ROLE OF AI IN HYBRID CLOUD OPTIMIZATION- AUTOMATING RESOURCE ALLOCATION AND COST EFFICIENCY

Abstract

The increasing adoption of hybrid cloud environments by enterprises necessitates efficient resource management and cost optimization. Traditional cloud management techniques struggle to balance workload distribution, scalability, and cost control, leading to inefficiencies and operational challenges. Artificial intelligence (AI) is emerging as a transformative force in hybrid cloud optimization, automating resource allocation while minimizing expenses. AI-driven approaches leverage machine learning models, predictive analytics, and intelligent automation to dynamically allocate resources based on workload demand, enhance performance, and reduce cloud infrastructure costs. The role of AI in hybrid cloud optimization, detailing how AI-powered solutions can improve workload distribution, enable predictive scaling, and enhance financial forecasting in cloud environments. We present case studies demonstrating AI's impact on cloud cost efficiency and operational agility across various industries. The paper highlights challenges in AI-driven cloud management, including data privacy concerns, computational overhead, and integration complexities. Future trends such as federated learning, green AI, and AI-driven multi-cloud orchestration are also discussed. By leveraging AI in hybrid cloud environments, organizations can achieve greater efficiency, resilience, and cost-effectiveness, ultimately transforming cloud computing operations. This study provides valuable insights into the evolving landscape of AI-driven hybrid cloud optimization and its potential for future advancements.