Network modeling is a fundamental tool in network research, design, and operation.
In the context of DTN, DRL can be leveraged to solve optimization problems without directly impacting the real-world network behavior.
1 code implementation • 29 Dec 2021 • José Suárez-Varela, Paul Almasan, Miquel Ferriol-Galmés, Krzysztof Rusek, Fabien Geyer, Xiangle Cheng, Xiang Shi, Shihan Xiao, Franco Scarselli, Albert Cabellos-Aparicio, Pere Barlet-Ros
Graph neural networks (GNN) have shown outstanding applications in many fields where data is fundamentally represented as graphs (e. g., chemistry, biology, recommendation systems).
Machine learning is gaining growing momentum in various recent models for the dynamic analysis of information flows in data communications networks.
In this paper we propose Enero, an efficient real-time TE solution based on a two-stage optimization process.
In our evaluation, we compare our MARL+GNN system with DEFO, a network optimizer based on Constraint Programming that represents the state of the art in TE.