Leveraging Machine Learning Techniques for Predictive Resource Management in Cloud Environments

Abstract

Efficient and intelligent resource management is a major challenge in cloud computing due to fluctuating workloads, large-scale infrastructures, and strict service-level agreement (SLA) constraints. Conventional reactive provisioning mechanisms often lead to resource over-provisioning, low utilization, and increased operational costs. This paper presents a predictive and autonomous cloud resource management framework based on machine learning and reinforcement learning techniques. The proposed approach follows a simplified workflow consisting of data collection and preparation, feature engineering, workload prediction, reinforcement learning-based optimization, and automated resource provisioning. Machine learning models are employed to forecast future workload patterns, while a Deep Q-Network (DQN) is applied to determine optimal scaling and resource allocation decisions based on system states and performance feedback. A continuous learning mechanism is incorporated through a feedback loop that updates prediction models and optimization policies in real time. Experimental evaluation in a simulated cloud environment demonstrates significant improvements in prediction accuracy, resource utilization efficiency, SLA compliance, and cost reduction compared to traditional threshold-based approaches. The results confirm that the proposed framework enables proactive, adaptive, and cost-effective cloud resource management.