Dynamic placement of virtual network functions based on model predictive control
Kota Kawashima, Tatsuya Otoshi, Yuichi Ohsita, Masayuki Murata · 2016
Dynamic placement of the virtual network functions (VNFs) is one of the promising approaches to handling time-varying demands; when demands are small, the energy consumption can be reduced by placing the VNFs to a small number of physical nodes and shutting down unused nodes. If the demands becomes large, the VNFs are migrated to allocate the sufficient resources. In the dynamic placement of the VNFs, it is important to avoid a large number of migrations at each time because the migration requires a large amount of bandwidth. In this paper, we propose a new method to dynamically place the VNFs to follow the traffic variation without migrating a large number of VNFs. Our method is based on the model predictive control (MPC). By applying the MPC to the dynamic placement of the VNFs, our method starts migration in advance by considering the predicted future demands. As a result, our method allocates sufficient resources to the VNFs without migrating a large number of VNFs at the same time even when traffic variation occurs. Through simulation, we demonstrate that our method handles the time variation of the demands without requiring a large number of migration at any time slot.