AI-Powered VM Selection: Amplifying Cloud Performance with Dragonfly Algorithm

Abstract

The convenience and cost-effectiveness offered by cloud computing have attracted a large customer base. In a cloud environment, inclusion of concept of virtualization requires careful management of resource utilization and energy consumption. With rapidly increasing consumer base of cloud data centres, it faces an overwhelming influx of VM requests. In cloud computing technology, the mapping these requests onto the actual cloud hardware is known as VM placement which is a significant areas of research. The article presents Dragonfly Algorithm integrated with Modified Best Fit Decreasing (DA-MBFD) is proposed to minimize the overall power consumption and the migration count. DA-MBFD uses MBFD for ranking VMs based on their resource requirement, then uses Minimization of Migration (MM) algorithm for hotspot detection followed by DA to optimize the replacement of VMs from the overutilized hosts. DA-MBFD is compared with few of the other existing techniques to show its efficiency. The comparative analysis of DA-MBFD against E-ABC, E-MBFD and MBFD-MM shows %improvement reflecting significant reduction for power consumption 8.21%, 8.6%, 6.77%, violations in service level agreement from 9.25%, 6.98% to 7.86%  and number of migrations 6.65%, 8.92%, 7.02%, respectively.

Keywords: CC, CDC, CSP, MBFD, VM, PM