GACA-VMP: Virtual Machine Placement Scheduling in Cloud Computing Based on Genetic Ant Colony Algorithm Approach

Cloud computing provides resources as services to customs by using virtualization technology. When virtual machines (VMs) are hosted on physical servers, there seem to be a chief concern that the way of intermediate and industrializing countries to deal with the great energy consumed by maintaining the servers in data centers. Therefore, the VM placement (VMP) problem is significant in both energy optimization and cloud computing. In this paper we propose a heuristic approach based on an improved ant colony algorithm (ACA) to solve the VMP problem, named as GACA-VMP. G is presented for genetic, that means the algorithm will conjunction with the genetic algorithm to solve the problem. By analyzing the pheromone during the ant movements, which is defined in ant placement between VM pairs, then the algorithm optimize the calculation of pheromone in load balancing theory, that is how we can select the suitable selection results. We evaluate the performance of the proposed GACA-VMP approach in solving VMP compared with the ones obtained with the simple ant colony algorithm (ACA), and the first-fit decreasing (FFD) algorithm. The results show that GACA-VMP can solve VMP more efficiently to select the opportune number of physical servers, together with remarkable resource utilization.