Anomaly Detection-Based Multilevel Ensemble Learning for CPU Prediction in Cloud Data Centers

In today's cloud computing era, accurate forecasting of CPU usage is crucial to maximize performance and energy efficiency in data centers. As cloud data centers become more complex and larger in scale, traditional predictive models may require enhancements to incorporate more sophisticated and comprehensive solutions. This paper presents a sophisticated multilevel learning framework specifically tailored to address the requirements of contemporary cloud data centers. The proposed framework synergistically combines anomaly detection and multilevel ensemble learning-based regression prediction to improve CPU usage prediction within cloud data centers. Various anomaly detection techniques are explored in the preliminary data processing stage to identify and address anomalies within the CPU usage trace. Subsequent phases employ multilevel ensemble-based prediction models for accurate data-driven forecasts. By conducting thorough assessments, our model exhibits substantial improvements in both the accuracy of predictions and its resilience to the inherent volatility of cloud environments. Our research provides the foundation for an improved method of predicting CPU utilization, paving the way for advancements in cloud computing resource management.