Open World Learning Graph Convolution for Latency Estimation in Routing Networks

TL;DR

This paper introduces a graph neural network approach for latency estimation in routing networks that generalizes well to unseen network configurations and scales, outperforming traditional models in accuracy and efficiency.

Contribution

A novel GNN-based model for network latency estimation that effectively extrapolates to unseen network sizes and configurations, maintaining stable performance across open-world inputs.

Findings

Outperforms conventional deep-learning models in accuracy

Maintains stable performance across different network sizes

Exhibits superior generalization to unseen network configurations

Abstract

Accurate routing network status estimation is a key component in Software Defined Networking. However, existing deep-learning-based methods for modeling network routing are not able to extrapolate towards unseen feature distributions. Nor are they able to handle scaled and drifted network attributes in test sets that include open-world inputs. To deal with these challenges, we propose a novel approach for modeling network routing, using Graph Neural Networks. Our method can also be used for network-latency estimation. Supported by a domain-knowledge-assisted graph formulation, our model shares a stable performance across different network sizes and configurations of routing networks, while at the same time being able to extrapolate towards unseen sizes, configurations, and user behavior. We show that our model outperforms most conventional deep-learning-based models, in terms of prediction accuracy, computational resources, inference speed, as well as ability to generalize towards open-world input.