Regression-Based Approach for Proactive Predictive Modeling of Efficient Cloud Cost Estimation.

Businesses increasingly lean on cloud-based solutions in the contemporary digital landscape, drawn by their scalability and adaptability. Navigating the financial intricacies of cloud subscription models, particularly when intertwined with software-defined systems, remains a formidable challenge. This challenge is accentuated by dynamic pricing structures, making accurate cost forecasting a critical but complex endeavor. This research unveils an innovative methodology designed to meticulously forecast the financial costs of cloud subscription tasks based on resource allocation characteristics. Our approach centers around a robust model carefully created through particular preparation and modeling stages that incorporate a pricing model for various virtual machines and utilize different advanced regression algorithms to navigate the complex world of cloud subscription services. The results of our study, which analyzed real cloud workloads, indicate that equipping businesses with a powerful predictive tool can lead to improved financial planning and strategic decision-making in their cloud operations. This study aims to guide businesses in the ever-changing field of cloud technology, focusing on promoting financial efficiency and improving decision-making strategies in their cloud initiatives.