Can Neural Networks Provide Latent Embeddings for Telemetry-Aware Greedy Routing?
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914327954456576 |
|---|---|
| author | Boltres, Andreas Freymuth, Niklas Neumann, Gerhard |
| author_facet | Boltres, Andreas Freymuth, Niklas Neumann, Gerhard |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_12798 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Can Neural Networks Provide Latent Embeddings for Telemetry-Aware Greedy Routing? Boltres, Andreas Freymuth, Niklas Neumann, Gerhard Machine Learning Artificial Intelligence Networking and Internet Architecture 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. |
| title | Can Neural Networks Provide Latent Embeddings for Telemetry-Aware Greedy Routing? |
| topic | Machine Learning Artificial Intelligence Networking and Internet Architecture |
| url | https://arxiv.org/abs/2602.12798 |