Optimization of VM migration and Energy Consumption using Genetic Algorithm

Abstract

Introduction: The substantial energy usage in the cloud computing system is a significant drawback both for the cloud providers along with the cloud service users. In order to decrease the amount of energy used, it is necessary to implement virtualization techniques.

Objectives: this paper employs a GA to optimize the migration of VMs and reduce energy usage in a cloud environment.

Methods: The VM consolidation method effectively handles cloud resources, meeting the needs of both cloud consumers and suppliers. Moreover, it helps in enhancing the efficiency of servers while simultaneously decreasing the excessive energy usage in data canters. Nevertheless, the unnecessary activities of the VM consolidation technique result in inadequate VM selection and improper VM assignment, causing low performance, QoS, and violations of SLAs. Data center management struggles with energy consumption. VM migration and placement works well for this. Data centers require energy-saving solutions without affecting other parameters.

Results: The performance of the proposed method has been evaluated utilizing factors like CPU usage, memory usage, network speed, power usage, and SLA breaches.

Conclusions: The comparative analysis of the proposed approach with existing methods highlights its effectiveness and trustworthiness.