FinOps-Driven Cloud Resource Optimization Using Predictive Analytics and Automated Workload Placement Strategies
DOI:
https://doi.org/10.30750/Keywords:
FinOps, cloud resource optimization, predictive analytics, automated workload placement, machine learning, cloud cost management, resource allocation, workload forecasting, cloud governance, infrastructure efficiencyAbstract
Cloud computing has become a fundamental component of modern enterprise infrastructure, enabling scalable services, flexible resource provisioning, and rapid application deployment. However, increasing cloud consumption, dynamic workloads, and complex pricing structures create significant challenges in controlling operational expenditure while maintaining performance and reliability. FinOps-driven cloud resource optimization integrates financial accountability, predictive analytics, and automated workload placement to improve resource utilization and cost efficiency. This research proposes an intelligent optimization framework that combines historical cloud usage data, real-time infrastructure metrics, workload characteristics, and pricing information to forecast resource demand and determine cost-effective deployment strategies. Predictive machine learning models estimate future CPU utilization, memory requirements, storage consumption, and network traffic, while an automated placement engine selects appropriate cloud resources according to cost, latency, availability, security, and service-level objectives. The framework incorporates continuous monitoring, anomaly detection, rightsizing recommendations, and feedback-driven optimization to support adaptive resource management across distributed cloud environments. A FinOps governance layer connects technical resource decisions with business budgets, allocation policies, and financial accountability. The proposed methodology evaluates optimization effectiveness using cloud expenditure, resource utilization, workload execution time, prediction accuracy, and service-level compliance. By combining predictive intelligence with automated decision-making, the framework aims to reduce unnecessary cloud spending, improve infrastructure efficiency, and strengthen collaboration between engineering, finance, and operations teams. The approach provides a foundation for sustainable, transparent, and performance-aware cloud financial management in enterprise computing environments.
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