Leveraging Imbalance and Ensemble Learning Methods for Improved Load Prediction in Cloud Computing Systems

Load prediction is a critical component of effective resource management in cloud computing. It ensures optimal performance and efficiency by anticipating overload, underload, and normal load periods. However, achieving accurate predictions remains challenging due to the highly dynamic and often non-linear workload patterns typical in cloud environments. Traditional methods, while helpful, have shown limitations in handling these complexities. Machine learning techniques, specifically imbalance and ensemble learning, have shown potential for improving prediction accuracy. Imbalance learning addresses the uneven distribution of load states, while ensemble learning combines multiple models to achieve better predictive performance. It is possible to create a more robust and accurate load prediction system by leveraging these two methods. This paper explores the application of imbalance and ensemble learning to improve load prediction in cloud computing systems. Through an experimental study, we illustrate how these techniques outperform traditional methods, offering potential improvements to the performance and efficiency of cloud computing operations.