DeepMPLS: Fast Analysis of MPLS Configurations Using Deep Learning

With the increasing complexity of communication networks and the resulting threat of disruptions of mission critical services due to manual misconfiguration, automated verification is becoming a key element in today's network operation. In particular, it has recently been shown that a polynomial-time, automated verification of the policy-compliance of network configurations is possible for the important class of MPLS networks, even under failures. However, this approach, while providing polynomial runtimes, is still fairly slow in practice and only allows to detect but not fix configurations. This paper proposes a novel approach to speed up the analysis of network properties as well as to suggest configuration changes in case a network property is not satisfied. More specifically, our solution, DeepMPLS, allows to predict if a network property is satisfiable, and if not, aims to present a counter example. We also show that DeepMPLS may be used to propose new prefix-rewriting rules in the MPLS configuration in order to make it satisfiable. DeepMPLS can hence be used for fast predictions, before more rigorous analyses are performed. DeepMPLS is based on a new extension of graph-based neural networks. Our prototype implementation, using Tensorflow, achieves low execution times and high accuracies in real-world network topologies.