OPTIMIZING CLOUD DATA MIGRATION COSTS THROUGH PREDICTIVE ANALYTICS: A FRAMEWORK AND IMPLEMENTATION GUIDE

Abstract

This article explores the implementation of predictive analytics for optimizing cloud data migration costs, presenting a comprehensive framework and practical implementation guide. The article addresses critical challenges organizations face during cloud migrations, including resource allocation inefficiencies, data transfer costs, and operational complexities. Through an analysis of various predictive analytics approaches, machine learning techniques, and implementation strategies, the article demonstrates how organizations can achieve significant cost reductions and improved operational efficiency. The article encompasses privacy-preserving mechanisms, adaptive learning capabilities, and sophisticated monitoring systems, providing a holistic approach to cost management. The article illustrates the tangible benefits of implementing predictive analytics in cloud migrations by examining real-world case studies across different industry sectors. This article also evaluates various implementation tools, including native cloud provider solutions, third-party platforms, and open-source frameworks, offering insights into their relative effectiveness. Furthermore, it explores emerging trends and future directions in cloud migration cost optimization, emphasizing the growing role of artificial intelligence and sustainable practices in shaping migration strategies.