An Artificial Intelligence and Deep Learning Driven Framework for Secure Load Balancing and Virtual Machine Migration in Cloud Computing Environments

Cloud computing environments require efficient resource allocation to maintain service performance, balance workloads, and support secure virtual machine (VM) migration. This paper proposes an artificial intelligence and deep learning-driven framework that integrates workload prediction, resource utilization analysis, migration-cost estimation, load balancing, and security-related mechanisms. A preliminary classification experiment was conducted using a synthetically generated workload dataset containing 50,000 records. The evaluated multilayer perceptron (MLP) achieved 78.59% accuracy and a weighted F1-score of 77.42% on the held-out test set. These results provide an initial assessment of workload classification under synthetic conditions. However, they do not establish real-cloud performance, VM migration efficiency, response-time improvements, or security effectiveness. The framework itself is a design proposal: its load-balancing, migration, and security components have not been implemented or evaluated, and they require separate evaluation using validated cloud traces and reproducible simulation or testbed experiments. Future work will investigate migration overhead, resource utilization, application response time, and security performance under controlled conditions.