A Multilevel Learning Model for Predicting CPU Utilization in Cloud Data Centers

In the contemporary era of cloud computing, efficient and precise prediction of CPU utilization ensures optimal performance and energy efficiency in data centers. Traditional predictive models often need to be improved as these data centers grow in complexity and scale, necessitating more nuanced and integrative solutions. This paper introduces an advanced multi-layered learning framework meticulously designed to meet the demands of modern cloud data centers. Our innovative approach synergistically combines anomaly detection, data clustering, and ensemble-based regression prediction. The Isolation Forest algorithm is used during the preliminary stage to identify and address anomalies within the data. Subsequent phases harness the K-Means clustering algorithm, refine data categorization based on recurrent CPU usage patterns, and employ multilevel ensemble-based prediction models for accurate forecasting rooted in historical and real-time data trends. Through comprehensive evaluations, our model demonstrates significant improvements in prediction accuracy and robustness against the dynamism inherent in cloud environments. Our research paves the way for a more resilient, proactive, and efficient approach to CPU utilization prediction, laying the foundational stone for future innovations in cloud computing resource management.