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MINT: Modeling GenAI Impact on Network Traffic — arXiv:2609.35742

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.35742
License (as archived): CC0-1.0
Archived: 2026-10-07T23:02:25.854Z
SHA-256 of the archived text: 908d33c664de5180214076b1d2fb0b8bc903f1faea32266afca230202a03ea87
Stable URL of this page: https://metraipolis.com/documento/dc_d004697c66de

How to cite

MINT: Modeling GenAI Impact on Network Traffic — arXiv:2609.35742 — arXiv (en), https://arxiv.org/abs/2609.35742 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_d004697c66de (Metraipolis archive dc_d004697c66de, sha256 908d33c664de5180214076b1d2fb0b8bc903f1faea32266afca230202a03ea87)

BibTeX

@misc{dcd004697c66de,
  title = {MINT: Modeling GenAI Impact on Network Traffic — arXiv:2609.35742},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.35742},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_d004697c66de; sha256 908d33c664de5180214076b1d2fb0b8bc903f1faea32266afca230202a03ea87},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.35742. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Generative AI (GenAI) is becoming a mainstream network workload, yet packet-level simulators lack measure\-ment-driven GenAI traffic models. Currently researchers must approximate GenAI services using traditional sources such as file transfer and video streaming, limiting realistic network evaluation of scheduling and capacity planning. We present MINT, a measurement and modeling framework for GenAI network traffic. Using an isolated net\-work-namespace capture pipeline, we collect client-side traces from three LLM providers across four modalities, cloud and edge servers, and wired and wireless network access points. We find that GenAI modalities exhibit distinct upload/download asymmetry and burst structures that differ from traditional applications. MINT clusters and models these burst regimes and validate empirical burst timing distribution behavior in ns-3 with normalized Wasserstein distances of 2--25\%. Our results also reveal that realistic packet bursts have significantly more variability than constant token generator models. MINT open-sources the first measurement-driven GenAI traffic model for packet-level network simulation.

Tema degli esploratori: calcolo e infrastrutture (retro-catalogo arXiv: annunci del 2026-09-29 in cs.NI).
Categorie arXiv: Networking and Internet Architecture (cs.NI) ; Systems and Control (eess.SY).
Pagina del lavoro: https://arxiv.org/abs/2609.35742 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-07T22:39:52.986Z dalla pagina https://arxiv.org/catchup/cs.NI/2026-09-29?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).