Proactive VNF provisioning with multi-timescale cloud resources: Fusing online learning and online optimization

Abstract

Network Function Virtualization (NFV) represents a new paradigm of network service provisioning. NFV providers acquire cloud resources, install virtual network functions (VNFs), assemble VNF service chains for customer usage, and dynamically scale VNF deployment against input traffic fluctuations. While existing literature on VNF scaling mostly adopts a reactive approach, we target a proactive approach that is more practical given the time overhead for VNF deployment. We aim to effectively estimate upcoming traffic rates and adjust VNF deployment a priori, for flow service quality assurance and resource cost minimization. We adapt online learning techniques for predicting future service chain workloads. We further combine the online learning method with a multi-timescale online optimization algorithm for VNF scaling, through minimization of the regret due to inaccurate demand prediction and minimization of the cost incurred by sub-optimal online decisions in a joint online optimization framework. The resulting proactive online VNF provisioning algorithm achieves a good performance guarantee, as shown by both theoretical analysis and simulation under realistic settings.