Cloud Cost Efficiency: Strategies and Implementation

I. ABSTRACT Clouds of resources manifest on demand, with every commodity charged per usage, but operational costs continue to skyrocket for organizations due to idle resources, over-provisioning, and improper workload management. This thesis undertakes cloud-costing optimization and embraces strategies for efficient resource utilizations, automation, and predictive monitoring. These are instance right-sizing, reserved, and spot instances, automated scheduling, serverless computing, and storage lifecycle management with networking optimization. This research puts even more emphasis on FinOps practices as financial accountability and machine learning methodologies that aim to forecast resource usage for a more proactive cost control procedure. A prototype framework facilitating reduction across major cloud vendors (AWS, Azure, GCP) via Infrastructure as Code and monitoring tools is built and evaluated. From the experiments, results show that such an optimization can reduce costs by 30-40% while preserving performance and service-level agreements. This research offered a comprehensive model towards sustainable and efficient cloud cost management.