Can Neural Networks Provide Latent Embeddings for Telemetry-Aware Greedy Routing?

• URL: http://arxiv.org/abs/2602.12798v1
• Date: Fri, 13 Feb 2026 10:31:09 GMT
• Title: Can Neural Networks Provide Latent Embeddings for Telemetry-Aware Greedy Routing?
• Authors: Andreas Boltres, Niklas Freymuth, Gerhard Neumann,
• Abstract summary: We propose emphPlacer, a novel algorithm using Message Passing Networks to transform network states into latent node embeddings.<n>These embeddings facilitate quick greedy next-hop routing without directly solving the all-pairs shortest paths problem.
• Score: 22.04675077514561
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Telemetry-Aware routing promises to increase efficacy and responsiveness to traffic surges in computer networks. Recent research leverages Machine Learning to deal with the complex dependency between network state and routing, but sacrifices explainability of routing decisions due to the black-box nature of the proposed neural routing modules. We propose \emph{Placer}, a novel algorithm using Message Passing Networks to transform network states into latent node embeddings. These embeddings facilitate quick greedy next-hop routing without directly solving the all-pairs shortest paths problem, and let us visualize how certain network events shape routing decisions.

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