GDDR: GNN-based data-driven routing


Type
Article
Change log
Authors
Hope, O 
Abstract

We explore the feasibility of combining Graph Neural Network-based policy architectures with Deep Reinforcement Learning as an approach to problems in systems. This fits particularly well with operations on networks, which naturally take the form of graphs. As a case study, we take the idea of data-driven routing in intradomain traffic engineering, whereby the routing of data in a network can be managed taking into account the data itself. The particular subproblem which we examine is minimising link congestion in networks using knowledge of historic traffic flows. We show through experiments that an approach using Graph Neural Networks (GNNs) performs at least as well as previous work using Multilayer Perceptron architectures. GNNs have the added benefit that they allow for the generalisation of trained agents to different network topologies with no extra work. Furthermore, we believe that this technique is applicable to a far wider selection of problems in systems research.

Description
Keywords
Reinforcement learning, Graph neural networks, Data-driven networks
Journal Title
Proceedings - International Conference on Distributed Computing Systems
Conference Name
Journal ISSN
1063-6927
Volume Title
2021-July
Publisher
IEEE
Rights
All rights reserved
Sponsorship
The authors would like to thank Kai Fricke for his input on GDDR project. This research was partly funded by the Alan Turing Institute.