Solving the 0/1 Knapsack Problem Using Metaheuristic and Neural Networks for the Virtual Machine Placement Process in Cloud Computing Environment

Abstract

Virtual machine placement (VMP) is carried out during virtual machine migration to choose the best physical computer to host the virtual machines. It is a crucial task in cloud computing. It directly affects data center performance, resource utilization, and power consumption, and it can help cloud providers save money on data center maintenance. To optimize various characteristics that affect data centers, VMs, and their runs, numerous VMP strategies have been developed in the cloud computing environment. This paper aims to compare the accuracy and efficiency of nine distinct strategies for treating the VMP as a knapsack problem. In the numerical analysis, we test out various conditions to determine how well the system works. We first illustrate the rate of convergence for algorithms, then the rate of execution time growth for a given number of virtual machines, and lastly the rate of development of CPU usage rate supplied by the nine methods throughout the three analyzed conditions. The obtained results reveal that the neural network algorithm performs better than the other eight approaches. The model performed well, as shown by its ability to provide near-optimal solutions to test cases.