Instruction-Guided AI-Driven Predictive Analytics for User-Level CPU Optimization and Overutilization Detection in Cloud Infrastructure Management

Abstract

Cloud computing provides scalable and cost-efficient infrastructure, yet effective CPU resource management continues to pose challenges. Overutilization of CPU resources can result in degraded performance, violations of service-level agreements (SLAs), and increased operational costs. While many existing solutions focus on optimizing system- or virtual machine-level performance, there has been limited exploration of user-level workload variability, where CPU demand often fluctuates significantly across different tenants. To address this gap, the study introduces an AI-driven predictive analytics framework designed to manage CPU overutilization at the user level in cloud environments. The framework combines machine learning with an instruction-guided decision engine that generates actionable optimization strategies for administrators, rather than relying on full automation. This human-in-the-loop approach enhances transparency and trust in operational decisions. The evaluation used two datasets, including synthetic workloads that represented light, medium, and heavy users, as well as real-world traces from the Bitbrains GWA-T-12 dataset. Random Forest and Logistic Regression models were trained using features such as average CPU utilization and provisioned capacity. CPU overutilization was defined as usage exceeding 90 percent of the allocated CPU. The results indicated that the Random Forest model achieved higher predictive accuracy, surpassing 90 percent, and reached an AUC value of 0.99, outperforming Logistic Regression. The decision engine then translated these predictions into optimization instructions, including actions like workload migration and CPU scaling. Overall, the findings demonstrate that combining user-level prediction with instruction-guided optimization improves CPU resource management, reduces the risk of overutilization, and enhances cloud system performance. This research contributes a practical, lightweight solution that advances predictive cloud resource management while preserving administrative oversight and system interpretability.