[2604.02927] Towards Near-Real-Time Telemetry-Aware Routing with Neural Routing Algorithms

Computer Science > Machine Learning arXiv:2604.02927 (cs) [Submitted on 3 Apr 2026] Title:Towards Near-Real-Time Telemetry-Aware Routing with Neural Routing Algorithms Authors:Andreas Boltres, Niklas Freymuth, Benjamin Schichtholz, Michael König, Gerhard Neumann View a PDF of the paper titled Towards Near-Real-Time Telemetry-Aware Routing with Neural Routing Algorithms, by Andreas Boltres and 4 other authors View PDF Abstract:Routing algorithms are crucial for efficient computer network operations, and in many settings they must be able to react to traffic bursts within milliseconds. Live telemetry data can provide informative signals to routing algorithms, and recent work has trained neural networks to exploit such signals for traffic-aware routing. Yet, aggregating network-wide information is subject to communication delays, and existing neural approaches either assume unrealistic delay-free global states, or restrict routers to purely local telemetry. This leaves their deployability in real-world environments unclear. We cast telemetry-aware routing as a delay-aware closed-loop control problem and introduce a framework that trains and evaluates neural routing algorithms, while explicitly modeling communication and inference delays. On top of this framework, we propose LOGGIA, a scalable graph neural routing algorithm that predicts log-space link weights from attributed topology-and-telemetry graphs. It utilizes a data-driven pre-training stage, followed by on-policy Rei...