An Energy-saving Data Transmission Approach based on Migrating Virtual Machine Technology to Cloud Computing

Abstract

Introduction: Over the past few years, researchers have greatly focused on increasing the electrical efficiency of large computer systems. Virtual machine (VM) migration helps data centers keep their pages' content updated on a regular basis, which speeds up the time it takes to access data. Offline VM migration is best accomplished by sharing memory without requiring any downtime. Objective: The objective of the paper was to reduce energy consumption and deploy a unique green computing architecture. The proposed virtual machine is transferred from one host to another through dynamic mobility. Methodology: The proposed technique migrates the maximally loaded virtual machine to the least loaded active node, while maintaining the performance and energy efficiency of the data centers. Taking into account the cloud environment, the use of electricity could continue to be critical. These large uses of electricity by the internet information facilities that maintain computing capacity are becoming another major concern. Another way to reduce resource use is to relocate the VM. Results: Using a non-linear forecasting approach, the research presents improved decentralized virtual machine migration (IDVMM) that could mitigate electricity consumption in cloud information warehouses. It minimizes violations of support agreements in a relatively small number of all displaced cases and improves the efficiency of resources. Conclusion: The proposed approach further develops two thresholds to divide overloaded hosts into massively overloaded hosts, moderately overloaded hosts, and lightly overloaded hosts. The migration decision of VMs in all stages pursues the goal of reducing the energy consumption of the network during the migration process. Given ten months of data, actual demand tracing is done through PlanetLab and then assessed using a cloud service.