Neural Networks for Rapid Network Configuration: A Concise Overview

Introduction

Network operators frequently need to adjust configurations to match evolving routing policies, but local changes can cause unexpected global effects, making manual updates complex and error‑prone.

Previous research has largely relied on synthesis techniques that use satisfiability modulo theories (SMT) solvers. Although effective for small instances, these tools are hand‑coded for specific protocols, may diverge from real router behavior, and become prohibitively slow on large networks.

Proposed Method

The paper proposes relaxing the exact configuration synthesis problem into an approximate learning objective suitable for neural methods. A neural network is trained to generate configurations that are expected to satisfy given specifications under existing routing protocols, either fully or partially.

Dataset Generation and Formatting

Real‑world topologies are sampled from the Topology Zoo repository. For each topology, BGP and OSPF parameters are randomly assigned, the protocols are simulated, and the resulting forwarding plane is computed. Specifications are then randomly selected from properties that hold on the forwarding plane, yielding pairs of specifications and topologies as inputs and the corresponding configurations as outputs.

The generated Fact Base contains topology structure, link weights, and specifications. An embedding scheme converts the Fact Base into a single graph where facts and constants become distinct nodes, and multiple edge types encode the positional relationships of constants within facts.

Model Architecture

The input Fact Base graph is first embedded, then processed by an Encoder GNN, a Processor GNN, and a Decoder network to produce a distribution over synthesis parameters. Both the Encoder and Processor consist of several stacked Graph Attention (GAT) layers.

Experiments

The model is evaluated on a test set of 2/8/16‑request configurations and compared against NetComplete, an SMT‑based synthesis solution. Results show that the neural synthesizer is 20‑to‑490× faster, with speed gains increasing for larger topologies. Despite the approximation, the generated configurations achieve an average consistency above 92% even on large topologies.

Conclusion

The study introduces a Neural Algorithmic Reasoning (NAR) architecture that encodes topology and configuration as graphs and leverages strong inductive bias in the iterative synthesis process. Empirically, the approach outperforms state‑of‑the‑art SMT‑based tools by up to 490× in speed while satisfying more than 93% of the specified constraints.