Artificial neural network model to predict affinity for virtual network functions

Abstract

Network Functions Virtualization (NFV) was proposed to migrate middleboxes that compose network services, such as firewalls and Network Address Translation (NAT) servers, from hardware to software running on Virtual Machines (VMs), commonly known as Virtualized Network Functions (VNFs). In NFV-enabled networks, VNFs can be chained in Forwarding Graphs (FGs) to provide services. These FGs establish the logical order in which network packets must traverse until reaching the end-service. In this scenario, network operators establish affinity and anti-affinity rules, which determine restrictions on the placement and chaining of VNFs according to how well or poorly VNFs operate together. To address the subject of identifying affinity relations in NFV-enabled networks, we previously proposed a mathematical model to measure the affinity between pairs of VNFs. However, that affinity model falls short for identifying affinity of VNFs not yet deployed, as they have no resource usage data to take into account. In this paper, we use artificial neural networks to predict affinity estimation for newly introduced VNFs, which still do not have usage data to be analyzed. This affinity neural network is trained using past affinity measurements, containing the data from VNFs, Physical Machines (PMs), and FGs of each measurement as features. We evaluate our solution by analyzing it over real usage data from a Cloud dataset, and conclude that neural networks can be used to provide affinity values for network operators, or NFV orchestrators, to plan the deployment of new VNFs.