The optimization of virtual resource allocation in cloud computing based on RBPSO

The virtual resource allocation in cloud computing is becoming a critical issue. In order to meet the task requirements of different users, virtual machines need to be placed on physical machines through virtualization technology in the data center. However, in this process, the total load balance, energy consumption, and resource utilization of physical machines should be considered. Therefore, two models are established for two different optimization targets, respectively. The first model is built to minimize the degree of load imbalance. The second model is built to maximize the resource utilization and minimize the energy consumption. To gain better results of virtual machines placement, we propose a new algorithm called resampled binary particle swarm optimization (RBPSO). To enhance the global search ability of BPSO, we add the re-sampling, mutation and small vibration process to it, named RBPSO, for the purpose of maintaining the diversity of the population, reducing redundant calculation and thereby improving the ability and efficiency of the algorithm. Then, the RBPSO is used to solve the deployment problem of virtual machines in cloud computing. The experiments show that the proposed model is reasonable and RBPSO performs better than BPSO and genetic algorithm (GA).