SLAV Mitigation and Energy-Efficient VM Allocation Technique Using Improvised Grey Wolf Optimization Algorithm for Cloud Computing

Abstract

A cloud computing environment is an attractive platform to facilitate large-scale sales processes for customers from various industries. However, extensive research has been carried out on the cloud data center to reduce power consumption due to operating costs and CO2 emissions. To provide a prominent solution for reducing power consumption in the cloud center, we proposed a novel VM allocation technique that overcomes the Service Level Agreement (SLA) violation and allocates the VM to the best host through Improvised Grey Wolf Optimization (IGWO) Algorithm. The proposed VM allocation technique takes into account host characteristics such as power consumption and CPU utilization to evaluate the best fit. After VM allocation, we evaluate the remaining capacity of CPU and RAM of the host, which enhances the resource utilization in the cloud center. The experiment was carried out on random datasets with different VMs. The performance of the proposed VM allocation technique is compared to the existing VM migration algorithm like Ant Colony Optimization (ACO) and PABFD (Power-Aware Best Fit Decreasing), the result shows that the proposed method provides a significant performance in terms of energy consumption, SLA violation, and several VM migrations. The proposed VM allocation method consumes low power and violates fewer SLAs, thus providing a green computing environment.