POSMAC: Powering Up In-Network AR/CG Traffic Classification with Online Learning

Fuente: arXiv
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Autores principales: Shirmarz, Alireza, Verdi, Fabio Luciano, Singh, Suneet Kumar, Rothenberg, Christian Esteve
Formato: Preprint
Publicado: 2025
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author Shirmarz, Alireza
Verdi, Fabio Luciano
Singh, Suneet Kumar
Rothenberg, Christian Esteve
author_facet Shirmarz, Alireza
Verdi, Fabio Luciano
Singh, Suneet Kumar
Rothenberg, Christian Esteve
contents In this demonstration, we showcase POSMAC1, a platform designed to deploy Decision Tree (DT) and Random Forest (RF) models on the NVIDIA DOCA DPU, equipped with an ARM processor, for real-time network traffic classification. Developed specifically for Augmented Reality (AR) and Cloud Gaming (CG) traffic classification, POSMAC streamlines model evaluation, and generalization while optimizing throughput to closely match line rates.
format Preprint
id arxiv_https___arxiv_org_abs_2502_00671
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle POSMAC: Powering Up In-Network AR/CG Traffic Classification with Online Learning
Shirmarz, Alireza
Verdi, Fabio Luciano
Singh, Suneet Kumar
Rothenberg, Christian Esteve
Networking and Internet Architecture
Distributed, Parallel, and Cluster Computing
In this demonstration, we showcase POSMAC1, a platform designed to deploy Decision Tree (DT) and Random Forest (RF) models on the NVIDIA DOCA DPU, equipped with an ARM processor, for real-time network traffic classification. Developed specifically for Augmented Reality (AR) and Cloud Gaming (CG) traffic classification, POSMAC streamlines model evaluation, and generalization while optimizing throughput to closely match line rates.
title POSMAC: Powering Up In-Network AR/CG Traffic Classification with Online Learning
topic Networking and Internet Architecture
Distributed, Parallel, and Cluster Computing
url https://arxiv.org/abs/2502.00671