Predictive Cloud Resource Optimization Using Machine Learning for Secure Enterprise Infrastructure Management

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-learningdriven
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.