| Texto completo | |
| Autor(es): |
Shirmarz, Alireza
;
Verdi, Fabio Luciano
;
Singh, Suneet Kumar
;
Rothenberg, Christian Esteve
Número total de Autores: 4
|
| Tipo de documento: | Artigo Científico |
| Fonte: | 2024 IEEE 10TH INTERNATIONAL CONFERENCE ON NETWORK SOFTWARIZATION, NETSOFT 2024; v. N/A, p. 9-pg., 2024-01-01. |
| Resumo | |
Augmented Reality (AR) real-time interaction between users and digital overlays in the real world demands low latency to ensure seamless experiences. To address computational and battery constraints, AR devices often offload processing-intensive tasks to edge servers, enhancing performance and user experience. With the increasing adoption and complexity of AR applications, especially in remote rendering, accurately classifying AR network traffic becomes essential for effective resource allocation. This paper explores two methods based on Decision Tree (DT) and Random Forest (RF) to classify network traffic among AR, Cloud Gaming (CG), and other categories. We rigorously analyze specific features to precisely identify AR and CG traffic. Our models demonstrate robust performance, achieving accuracy rates ranging from 88.40% to 94.87% against pre-existing datasets. Moreover, we contribute with a novel dataset encompassing AR and CG traffic, curated specifically for this study and made publicly available to facilitate reproducible research in AR network traffic classification. (AU) | |
| Processo FAPESP: | 21/00199-8 - Redes e serviços inteligentes rumo 2030 (SMARTNESS) |
| Beneficiário: | Christian Rodolfo Esteve Rothenberg |
| Modalidade de apoio: | Auxílio à Pesquisa - Programa Centros de Pesquisa em Engenharia |