International Journal of Science, Engineering and TechnologyAuthors: Pranay Saxena
Abstract: Predictive cloud scaling is emerging as a transformative approach for managing operational costs in cloud environments. By proactively forecasting resource demands based on historical and real-time data, predictive scaling enables organizations to allocate cloud resources more efficiently, minimizing waste and reducing unnecessary expenses. The traditional reactive scaling methods often result in delayed responses to workload spikes or under-utilization during off-peak periods, causing either service degradation or inflated costs. Predictive cloud scaling addresses these challenges by leveraging advanced machine learning algorithms and analytics to anticipate demand fluctuations and automate resource adjustments accordingly. This article explores the critical role of predictive cloud scaling in operational cost management, including its mechanisms, benefits, and implementation challenges. It examines the interplay between predictive scaling and cost optimization strategies, highlighting how predictive analytics can enhance cloud resource utilization while maintaining service quality. Through a comprehensive review of existing technologies, industry practices, and use cases, the article provides a detailed understanding of how predictive cloud scaling can drive substantial financial and operational efficiencies. It also discusses the potential risks and best practices to ensure accuracy and reliability in predictive models. As more enterprises adopt cloud computing for its scalability and agility, predictive scaling becomes an essential technique to control escalating operational costs and optimize cloud investments. The insights presented herein are valuable for cloud architects, IT finance teams, and decision-makers aiming to harness predictive capabilities for smarter cloud cost management.
DOI: http://doi.org/10.5281/zenodo.17709625
