NeuroViNE: A Neural Preprocessor for Your Virtual Network Embedding Algorithm

Abstract

Network virtualization enables increasingly diverse network services to cohabit and share a given physical infrastructure and its resources, with the possibility to rely on different network architectures and protocols optimized towards specific requirements. In order to ensure a predictable performance despite shared resources, network virtualization requires a strict performance isolation and hence, resource reservations. Moreover, the creation of virtual networks should be fast and efficient. The underlying NP-hard algorithmic problem is known as the Virtual Network Embedding (VNE) problem and has been studied intensively over the last years. This paper presents NeuroViNE, a novel approach to speed up and improve a wide range of existing VNE algorithms: NeuroViNE is based on a search space reduction mechanism and preprocesses a problem instance by extracting relevant subgraphs, i.e., good combinations of substrate nodes and links. These subgraphs can then be fed to an existing algorithm for faster and more resource-efficient embeddings. NeuroViNE relies on a Hopfield network, and its performance benefits are investigated in simulations for random networks, real substrate networks, and data center networks.