Adaptive VNF Scaling and Flow Routing with Proactive Demand Prediction

With the evolution of Network Function Virtual-izaiton (NFV), enterprises are increasingly outsourcing their network functions to the cloud. However, using virtualized network functions (VNFs) to provide flexible services in today's cloud is challenging due to the inherent difficulty in intelligently scaling VNFs to cope with traffic fluctuations. To best utilize cloud resources, NFV providers need to dynamically scale the VNF deployments and reroute traffic demands for their customers. Since most existing work is reactive in nature, we seek a proactive approach to provision new instances for overloaded VNFs ahead of time based on the estimated flow rates. We formulate the VNF provisioning problem in order that the cost incurred by inaccurate prediction and VNF deployment is minimized. In the proposed online algorithm, we first employ an efficient online learning method which aims at minimizing the error in predicting the service chain demands. We then derive the requested instances with adaptive processing capacities and call two other algorithms for new instance assignment and service chain rerouting, respectively, while achieving good competitive ratios. The joint online algorithm is proven to provide good performance guarantees by both theoretical analysis and trace-driven simulation.