Leveraging Artificial Intelligence for smart cloud migration, reducing cost and enhancing efficiency

Chanthati, Sasibhushan Rao (2025) Leveraging Artificial Intelligence for smart cloud migration, reducing cost and enhancing efficiency. World Journal of Advanced Engineering Technology and Sciences, 15 (1). 033-045. ISSN 2582-8266

Cloud computing has become a critical component of modern IT infrastructure, offering businesses scalability, flexibility, and cost efficiency. Unoptimized cloud migration strategies can lead to significant financial waste due to inefficient resource allocation, redundant workloads, and unpredictable cloud expenses. Traditional methods often rely on static provisioning and manual decision-making, leading to suboptimal cloud resource utilization. This research introduces an AI-driven framework for intelligent cloud planning and migration aimed at reducing cloud costs while maintaining high performance and compliance standards. The proposed framework leverages machine learning (ML), deep learning (DL), and reinforcement learning (RL) techniques to automate workload distribution, real-time scaling, and dynamic cost optimization. It integrates Predictive Analytics Engine: Uses AI models (Long Short-Term Memory LSTMs, CNNs, and Transformers) to analyze historical workload data and forecast future resource demands. Optimization Algorithm: Implements AI-driven cost minimization functions, optimizing resource allocation while maintaining Quality of Service (QoS). Automated Migration Engine: Reduces manual intervention by executing AI-based cloud workload transfers efficiently. Security and Compliance Module: Uses explainable AI (XAI) and federated learning to maintain cloud security, privacy, and regulatory compliance. A proof of concept (PoC) is developed and evaluated across multiple cloud platforms (AWS, Azure, Google Cloud) with real-world datasets. Experimental results indicate that the AI-driven framework achieves: Cost savings of up to 42% compared to traditional cloud migration strategies. Resource utilization improvement by 53%, ensuring minimal wastage. Reduction in system downtime by 75%, leading to higher reliability. Reduction in manual intervention by 85%, automating resource scaling and load balancing. The research paper also presents real-world case studies across finance, healthcare, e-commerce, and manufacturing sectors, demonstrating the tangible impact of AI-based cloud optimization. This research explores future advancements in cloud computing, including Quantum AI for cloud workload acceleration, Blockchain for transparent cloud cost auditing, and Decentralized AI governance for multi-cloud management. This study contributes to the growing field of AI-driven cloud cost optimization, providing a roadmap for enterprises, cloud architects, and AI researchers to achieve cost-efficient, high-performance, and automated cloud management.