GDDR: GNN-based data-driven routing
| Autor: | Eiko Yoneki, Oliver Hope |
|---|---|
| Přispěvatelé: | Yoneki, Eiko [0000-0002-5552-4536], Apollo - University of Cambridge Repository |
| Rok vydání: | 2021 |
| Předmět: |
Networking and Internet Architecture (cs.NI)
FOS: Computer and information sciences Computer Science - Machine Learning Computer science business.industry Distributed computing Knowledge engineering Network topology Machine Learning (cs.LG) Data-driven Graph neural networks Computer Science - Networking and Internet Architecture Traffic engineering Multilayer perceptron Reinforcement learning Selection (linguistics) Data-driven networks Routing (electronic design automation) business |
| Zdroj: | ICDCS |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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