Enhancing Virtual Machine Migration in Cloud Environments

Virtual Machine Migration (VMM) is a crucial aspect of cloud computing that allow organizations to efficiently manage their computing resources. However, this process presents several challenges, including resource contention, network bandwidth limitation, and compatibility issues which can lead to increased migration time, service disruption, and even data loss. To address these challenges, this research paper recapitulates VM Migration Techniques, objectives, its issues. In this study a model is proposed that has the potential to minimize the downtime and the other issues associated with VM migration process in cloud computing. The proposed model employs machine learning technique to estimate how much compute and network traffic a virtual machine will require when relocated from one real machine to another. The model is then trained with an appropriate machine learning technique to predict the required processing power, network bandwidth, and computing resources during the migration, which aids in maintaining VM downtime to a minimum. It enhances the VMM procedure and guarantees the virtual machine runs well on the new hardware.