Machine Learning-Driven Cloud Resource Forecasting and Intelligent Capacity Management for Secure Enterprise Infrastructure

Abstract

Predictive cloud resource optimization has become increasingly important for enterprises seeking to balance infrastructure performance, operational cost, scalability, and security. Conventional cloud resource management approaches primarily rely on reactive monitoring and threshold-based scaling, which can result in resource overprovisioning, performance degradation, unnecessary expenditure, and delayed responses to emerging infrastructure risks. This research proposes a machine-learning-driven framework for predictive cloud resource optimization that integrates workload forecasting, resource utilization prediction, anomaly detection, and security-aware infrastructure management. The proposed approach analyzes historical and real-time telemetry, including CPU utilization, memory consumption, network traffic, storage activity, application response time, workload intensity, and security events, to predict future resource requirements and identify abnormal infrastructure behavior. Machine learning models are employed to forecast workload demand and dynamically recommend or execute resource allocation decisions across enterprise cloud environments. A security-aware optimization layer incorporates risk scores and policy constraints into resource provisioning decisions, ensuring that optimization does not compromise confidentiality, availability, or compliance requirements. The methodology evaluates predictive accuracy, resource utilization, response time, infrastructure cost, scaling efficiency, and security effectiveness. The proposed framework aims to provide proactive, adaptive, and intelligent cloud infrastructure management capable of improving resource efficiency while maintaining reliable and secure enterprise operations.