Artificial Intelligence Based Virtual Machine Allocation and Migration Policy using Improved MBFD

Abstract

With rising demand and an increase in the number of servers in data centres, cloud computing is a new era of technology that is based on pay-per-use. As the number of servers rises, so do the virtual machines (VMs), which must be distributed among physical hosts to satisfy client demands. These VMs must be appropriately assigned; otherwise, their erroneous allocation increases energy consumption. This can be avoided by reallocating these VMs to a suitable physical host that can accommodate their needs. The mechanism for allocating and migrating virtual machines is presented in this work. With the aid of our suggested optimization technique, the uniqueness in the two primary virtualization processes- VM allocation and VM migration-was explained in this study. In terms of VM allocation and migration, the proposed study reduced energy consumption relative to earlier research. The proposed work is broken down into two main sections: 1. VM allocation using Improved MBFD. b. SVM-based allocation verification to cut down on false migrations. This article is divided into five sections: Section 1 introduces virtualization, VM allocation, and migration; A brief summary of the literature is presented in Section 2, while Section 3 provides a suggested allocation and migration policy using an improved MBFD; and Section 4 offers results and discussion, followed by a conclusion and discussion of the article's future directions.