Improving Cloud Efficiency: A Machine Learning-Based Stacking Model for CPU Utilization Prediction
Ashir Javeed, Anton Borg, Håkan Grahn, Lars Lundberg, Dhyey Patel, Sogand Shirinbab · 2025
With the rapid growth of internet technologies, IT businesses are transferring to cloud-based systems, and cloud-based services are in high demand among internet users. Therefore, appropriate allocation of resources in cloud computing environments is essential. The companies can reduce costs by saving energy by dynamically scaling up or down the number of active servers. In this context, this study presents a machine learning-based model for accurate prediction of CPU utilization. Previous studies employed timestamp-based data to predict CPU utilization in cloud computing, while the proposed work uses incoming user requests to predict CPU workload so that a timely decision can be made to scale up or scale down the servers in a cloud computing environment. The proposed model is based on several machine learning algorithms that are stacked into a single model called the stacking model for CPU workload prediction. The effectiveness of the proposed stacking model was tested on several evaluation metrics to validate its performance. Furthermore, the performance of the proposed stacking model is also compared with other state-of-the-art machine learning models such as support vector machines (SVM), decision trees (DT), random forests (RF), gradient boosting, and extreme gradient boosting (XGBoost).