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Found 7 papers, 3 papers with code
RouteNet-Erlang: A Graph Neural Network for Network Performance Evaluation
Network modeling is a fundamental tool in network research, design, and operation.
Accelerating Deep Reinforcement Learning for Digital Twin Network Optimization with Evolutionary Strategies
In the context of DTN, DRL can be leveraged to solve optimization problems without directly impacting the real-world network behavior.
Graph Neural Networks for Communication Networks: Context, Use Cases and Opportunities
Graph neural networks (GNN) have shown outstanding applications in many fields where data is fundamentally represented as graphs (e. g., chemistry, biology, recommendation systems).
Physics Constrained Flow Neural Network for Short-Timescale Predictions in Data Communications Networks
Machine learning is gaining growing momentum in various recent models for the dynamic analysis of information flows in data communications networks.
ENERO: Efficient Real-Time WAN Routing Optimization with Deep Reinforcement Learning
In this paper we propose Enero, an efficient real-time TE solution based on a two-stage optimization process.
Is Machine Learning Ready for Traffic Engineering Optimization?
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.
Flow Neural Network and Flow-Structured Data Representation
Traffic flows are the most fundamental components in a communication networking system.
