NetGenius: Routing Configuration Recommendation Based on Graph Neural Network

Configurations deployed in routers govern the routing and addressing of IP-based networks. Automated tools for synthesizing and verifying configurations have been deployed to replace manual configurations, preventing network outages from misconfigurations. However, these tools offer limited assistance in real production networks as manual configuration remains the primary and preferred approach for daily operations. To address this issue, we present NetGenius, a configuration recommendation tool that assists operators in manually editing network configurations like a code editor. NetGenius employs a graph neural network to estimate the importance of neighboring commands to the center command, providing recommendations based on importance scores when the center command is used as input. First, NetGenius leverages a generic graph model named Knowledge Graph to model existing configurations and derives configuration features for each center command by composing it with its neighbors. Subsequently, NetGenius conducts a general approach based on the graph neural network to estimate and calculate corresponding feature importance scores. Finally, NetGenius uses a recommendation mechanism to recommend configurations based on importance scores while adhering to specific constraints. We extensively evaluate the performance of NetGenius using real-world configurations from different vendors. The experimental results demonstrate that NetGenius achieves the highest recommendation accuracy of 98.59% across various datasets. Moreover, when applied to a large-scale network comprising <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$152,475$</tex-math> </inline-formula> configuration commands, NetGenius completes its training within 6 seconds and generates recommendations in 5 milliseconds.