In recent years, virtualization platforms have not only been used to integrate data from traditional application systems but have also actively collected Internet of Things (IoT) data from various network transmissions. To address the challenges of real-time monitoring for key metrics on virtualization platforms, this study proposes an optimal machine learning training model that combines semi-supervised Self-Training algorithms with supervised ensemble algorithms. In the application of semi-supervised training learning algorithms, this study utilizes a Self-Training learning algorithm to label a large number of unlabeled virtual machine operational states with a small amount of labeled data, laying the foundation for subsequent model construction. Subsequently, an ensemble learning classification algorithm is introduced to further validate and identify learning models suitable for generalization. Empirical evaluations show that the RandomForest algorithm serves as the optimal base estimator for Self-Training, while the Bagging algorithm is the optimal choice for ensemble learning. The synergy of these two achieves an accuracy exceeding 99%, enabling the model to accurately differentiate between various operational states such as normal operation, resource insufficiency, and faults. Finally, the integrated training model is deployed to a dashboard, displaying the real-time operational status of virtual machines through different colored lights. Simultaneously, operational status information is communicated to stakeholders through various media, further improving coordination, decision-making, and resource allocation issues on the virtualization platform. This study provides an efficient and feasible solution for monitoring and managing virtualization platforms.
