Scalable Provisioning of Virtual Network Functions via Supervised Learning

Abstract

Network Function Virtualization (NFV) is opening new opportunities for both the business and the research community. As the need to softwarize functions grows, managing the underlying hosting infrastructure faces new challenges. In this paper, we focus on one of these challenges: the ability to provision enough virtualizable infrastructure resources to guarantee smooth and responsive network and application operations. To this aim, we learn from observed patterns of requests to an infrastructure hosting virtual network functions, and we model the problem of appropriately scaling resources to provision them. Using months of real Internet traffic requests, we train and compare the performance of several (classical and more recent) learning algorithms. Our goal is to predict future NFV requests to proactively provision our infrastructure using constraint optimization. Our results, obtained with simulations and with a prototype that deploys Linux containers, show both expected and surprising results, and aims at fostering debates on when and if the juice of (supervised) deep learning techniques is worth the squeeze.