AI and Cloud Computing for Enhanced Virtualization and Containerization

Abstract

This research paper aims at analysing the application of artificial intelligence and deep learning techniques in the cloud computing paradigm especially in virtualization and containerization. Since cloud computing has been rapidly integrated into organizations, it is necessary to address the problem of resource management. This study evaluates four deep learning models—Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Autoencoders—across key performance metrics: which are accuracy, precision, recall, and F1 score, for example. The findings suggest that the CNN had the best accuracy of 92% for identifying performance bottlenecks, while the LSTM had the second-best accuracy of 91% for forecasting. The RNN and Autoencoder also had a good performance in terms of predicting resource utilization and detecting abnormal behavior respectively. Consequently, it is suggested that these algorithms can significantly enhance the operational, security, and resource management efficiency of cloud computing and these may be valuable for further research and practical applications.