Migration-Based Load Balance of Virtual Machine Servers in Cloud Computing by Load Prediction Using Genetic-Based Methods

This paper presents a two-stage genetic mechanism for the migration-based load balance of virtual machine hosts (VMHs) in cloud computing. Previous methods usually assume this issue as job-assignment optimization problem and only consider current VMHs' loads; however, without considering loads VMHs after balancing, these can gain limited effectiveness real environments. In study, two genetic-based are integrated presented. First, performance models machines (VMs) extracted from their creating parameters corresponding measured computing environment. The gene expression programming (GEP) is applied generating symbolic regression that describe VMs used predicting load-balance. Secondly, with VMH estimated by GEP, algorithm considers future decides an optimal VM-VMH assignment migrating performing proposed evaluated cloud-computing environment, Jnet, wherein implemented centralized balancing mechanism. experimental results show our method outperforms previous methods, such heuristics statistics regression.