Abstract: Effective resource estimation is essential in cloud computing to minimize operational costs, optimize performance, and enhance user satisfaction. This study proposes a comprehensive framework for virtual machine optimization in cloud environments, focusing on predictive resource management to improve resource efficiency and system performance. The framework integrates real-time monitoring, advanced resource management techniques, and machine learning-based predictions. A simulated environment is deployed using PROXMOX, with Prometheus for monitoring and Grafana for visualization and alerting. By leveraging machine learning models, including Random Forest Regression and LSTM, the framework predicts resource usage based on historical data, enabling precise and proactive resource allocation. Results indicate that the Random Forest model achieves superior accuracy with a MAPE of 2.65%, significantly outperforming LSTM's 17.43%. These findings underscore the reliability of Random Forest for resource estimation. This research demonstrates the potential of predictive analytics in advancing cloud resource management, contributing to more efficient and scalable cloud computing practices.

Rim Doukha and Abderrahmane Ez-zahout, “Enhanced Virtual Machine Resource Optimization in Cloud Computing Using Real-Time Monitoring and Predictive Modeling” International Journal of Advanced Computer Science and Applications(IJACSA), 16(2), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160267

@article{Doukha2025,
title = {Enhanced Virtual Machine Resource Optimization in Cloud Computing Using Real-Time Monitoring and Predictive Modeling},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160267},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160267},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {2},
author = {Rim Doukha and Abderrahmane Ez-zahout}
}