RouteNet: Leveraging Graph Neural Networks for network modeling and optimization in SDN

Abstract

Network modeling is a key enabler to achieve efficient network operation in future self-driving Software-Defined Networks. However, we still lack functional network models able to produce accurate predictions of Key Performance Indicators (KPI) such as delay, jitter or loss at limited cost. In this paper we propose RouteNet, a novel network model based on Graph Neural Network (GNN) that is able to understand the complex relationship between topology, routing, and input traffic to produce accurate estimates of the per-source/destination per-packet delay distribution and loss. RouteNet leverages the ability of GNNs to learn and model graph-structured information and as a result, our model is able to generalize over arbitrary topologies, routing schemes and traffic intensity. In our evaluation, we show that RouteNet is able to predict accurately the delay distribution (mean delay and jitter) and loss even in topologies, routing and traffic unseen in the training (worst case MRE=15.4%). Also, we present several use cases where we leverage the KPI predictions of our GNN model to achieve efficient routing optimization and network planning.

12 pages

References in corpus (5)

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Cited by in corpus (15)

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• ENERO: Efficient Real-Time WAN Routing Optimization with Deep Reinforcement Learning
• The Graph Neural Networking Challenge: A Worldwide Competition for Education in AI/ML for Networks
• Metarobotics for Industry and Society: Vision, Technologies, and Opportunities
• Data-driven Predictive Latency for 5G: A Theoretical and Experimental Analysis Using Network Measurements
• Optimizing Task Placement and Online Scheduling for Distributed GNN Training Acceleration
• Network Calculus with Flow Prolongation -- A Feedforward FIFO Analysis enabled by ML
• QT-Routenet: Improved GNN generalization to larger 5G networks by fine-tuning predictions from queueing theory
• SCV-GNN: Sparse Compressed Vector-based Graph Neural Network Aggregation
• Towards a Robust Transport Network With Self-adaptive Network Digital Twin
• RELiQ: Scalable Entanglement Routing via Reinforcement Learning in Quantum Networks
• From Simulation to Deep Learning: Survey on Network Performance Modeling Approaches
• Cross-network transferable neural models for WLAN interference estimation