Genetic algorithm with self adaptive immigrants for effective virtual machine placement in cloud environment

Abstract

In cloud environments, optimization of resource utilizations is one among the predominant challenges. The two sub-research topics are cloud resource prediction and allocation. A few contributions to virtual machine (VM) placement techniques have been identified in the literature. In order to efficiently put up the virtual machine (VM) on the physical machine (PM), a Self Adaptive Immigrants with Genetic Algorithm (SAI-GA) is presented in this study. Based on CPU and memory usage, the proposed technique would forecast the best PM for each VM. The algorithm will adjust itself with the appropriate immigrant based on the history of past VM placement to find the best VM placement. In this paper, the VM live dataset from the CSAP lab at SNU in Korea has been used. For the purpose of demonstrating the significance of the findings, a number of non-parametric tests were used to evaluate how well the proposed SAI-GA performed. The outcomes demonstrate that the suggested approach makes a considerable contribution to the placement of VMs in cloud environments.