Optimizing Cloud Costs with Machine Learning: Predictive Resource Scaling Strategies

This article presents a case study on the optimization of cloud expenses using machine learning and predictive resource scalability with Amazon Web Services (AWS). Estimate prospective resource requirements, analyze historical data, and automate scaling decisions. This study used various sources to investigate cloud management machine learning issues, ethics, and best practices. Containerized microservices, cloud application performance, and auto-scaling are discussed in the references. We anticipate that serverless architectures and peripheral computing will affect cloud cost optimization in the future. Amid the transformation of cloud management brought about by machine learning, explainable AI, autoML integration, and enhanced predictive analytics are emerging. This study underscores the revolutionary potential of machine learning for organizations traversing the dynamic cloud computing market and its crucial function in optimizing cloud expenditures after a comprehensive examination of essential aspects. Prevailing cost-related issues in the cloud include over-provisioning, complex pricing models, and lack of visibility/control over expenses. Advancing cloud resources, particularly through predictive resource scaling strate- gies, addresses these challenges by optimizing resource allocation based on real-time demand patterns, thus reducing unused capacity and optimizing costs.