Leveraging Machine Learning Technique for Virtual Machine Migration in Cloud Computing

Virtual Machine (VM) migration in cloud environments is essential for maintaining high availability, load balancing, and efficient resource utilization. Pre-copy VM migration, a prevalent technique, often faces challenges like high data transfer volumes, extended migration times, and noticeable downtime. This study proposed a machine learning-based improved VM migration technique. Our approach significantly reduces data transferred by analyzing and predicting memory pages based on their characteristics, thereby decreasing total migration time and minimizing service disruption. Experimental results demonstrate that our technique outperforms existing pre-copy methods by reducing data transfer by up to 69% and migration time by 33.33% while diminishing downtime by 42%.