Predictive CPU Utilization Modeling in Cloud Operating Systems Using Machine Learning

Abstract

Efficient CPU utilization is vital for maintaining performance and reducing operational costs in cloud computing environments. As workloads grow increasingly dynamic and complex, traditional resource allocation methods struggle to adapt in real time. This paper explores the use of machine learning techniques to model and predict CPU utilization within cloud operating systems. By analyzing historical usage data, workload characteristics, and system metrics, we construct predictive models that provide real-time insights for proactive resource management. Our proposed framework leverages supervised learning algorithms, including random forests and neural networks, to capture non-linear trends and temporal dependencies. The experimental results demonstrate significant improvements in prediction accuracy over baseline methods, highlighting the feasibility of machine learning-based forecasting for optimizing cloud CPU resource allocation.