Scaling Graph-based Deep Learning models to larger networks
It addresses a practical limitation for network control and management applications, but appears incremental as it builds on existing GNN methods to improve scalability.
This paper tackles the scalability problem of Graph Neural Networks (GNNs) in networking by presenting a solution that effectively scales to larger networks with higher link capacities and aggregated traffic, addressing a limitation identified in the Graph Neural Networking challenge 2021.
Graph Neural Networks (GNN) have shown a strong potential to be integrated into commercial products for network control and management. Early works using GNN have demonstrated an unprecedented capability to learn from different network characteristics that are fundamentally represented as graphs, such as the topology, the routing configuration, or the traffic that flows along a series of nodes in the network. In contrast to previous solutions based on Machine Learning (ML), GNN enables to produce accurate predictions even in other networks unseen during the training phase. Nowadays, GNN is a hot topic in the Machine Learning field and, as such, we are witnessing great efforts to leverage its potential in many different fields (e.g., chemistry, physics, social networks). In this context, the Graph Neural Networking challenge 2021 brings a practical limitation of existing GNN-based solutions for networking: the lack of generalization to larger networks. This paper approaches the scalability problem by presenting a GNN-based solution that can effectively scale to larger networks including higher link capacities and aggregated traffic on links.