Bi-Objective Virtual Machine Placement using Hybrid of Genetic Algorithm and Particle Swarm Optimization in Cloud Data Center

Efficient resource management through the virtual machine placement (VMP) is a great concern in data centers. The Biobjective VPM is a representation of multi-objective combinatorial optimization problem. Energy or cost minimization of cloud data center is highly dependent upon the VMP policy. Allocating the set of virtual machines (VMs) to the set of suitable physical machines (PMs), while considering the cost, CPU utilization, number of active servers and energy consumption of cloud computing, defines the VMP process. In this paper, a cloud model simulated with evolutionary algorithms (genetic algorithm (GA), Particle Swarm Optimization (PSO), and hybrid GA-PSO (HGAPSO)) for the suitable VMP with the objectives of minimizing Energy consumption, and number of active servers, while considering the CPU utilization, RAM, network bandwidth etc. The HGAPSO produced the optimum result and outperformed the other two algorithms.