Predicted Affinity Based Virtual Machine Placement in Cloud Computing Environments

Abstract

In cloud data centers, an appropriate Virtual Machine (VM) placement has become an effective method to improve the resource utilization and reduce the energy consumption. However, most current solutions regard the VM placement as a bin-packing problem and each VM is seen as a single object. None of them have taken the relationships between VMs into consideration, which supplies us with a kind of new perspective. In this paper, we provide a model which explores the relationships for every two VMs based on the resource requirement provided by ARIMA prediction. This model evaluates the volatility of resource utilization after putting the two VMs on the same host and we call this model as affinity model. Based on the affinity model, VMs will be placed on those hosts that have the highest affinity with them. Therefore, we call it as Predicted Affinity based Virtual Machine Placement Algorithm (PAVMP). The advantages of PAVMP are showed by comparing it with other VM placement algorithms on CloudSim simulation platform with the PlanetLab and Google workload trace.