Revolutionizing Cloud Infrastructure Scaling: A Gap Detection Approach

Abstract

In the dynamic landscape of cloud computing, efficiently scaling compute and storage resources remains critical for organizations striving to optimize costs while meeting fluctuating demand. Traditional auto-scaling methods, which rely on setting minimum and maximum limits, often fail to account for nuanced growth patterns and can lead to either resource shortages or excess capacity. This comprehensive article introduces a novel approach to cloud infrastructure scaling by distinguishing between organic and inorganic growth while implementing an innovative gap detection methodology. The article examines how integrating real-time metrics with predictive analytics enables proactive resource management, allowing organizations to secure long-term reservations and optimize costs. By incorporating both organic and inorganic growth factors into the scaling strategy, this research demonstrates the creation of a holistic resource management system that enhances operational efficiency while significantly reducing costs through advanced techniques such as machine learning, dynamic resource allocation, automated reservation systems, and policy-driven scaling decisions.