DeepBGP: A Machine Learning Approach for BGP Configuration Synthesis

Border Gateway Protocol (BGP) is the standard inter-domain routing protocol that is used to exchange reachability information among Wide Area Networks (WANs). BGP is a policy-based routing protocol that introduces a lot of flexibility. However, this flexibility increases the configuration complexity. In this research, we introduce DeepBGP as a neural network-based system that synthesizes network configuration given a high-level operator intent. We adopt Graph Neural Network (GNN) to represent network topology and generate partial network configuration. A validation unit is then used to calculate a reward based on which an Evolution Strategies (ES) optimizer updates neural network parameters. Since ES does not require backpropagation, they provide a significant reduction in calculation time. Further, the recent advances in deep learning with strong hardware acceleration and the parallelization capabilities offered by ES provide great potential in scaling the proposed solution to larger topologies. We demonstrate experimentally that DeepBGP can generate a network-wide configuration for both Huawei and Cisco devices while fulfilling operator requirements. We also show how Deep-BGP scales when the network size increases, and how hardware acceleration could improve the scalability of the system.