Deep Learning Forwarding in NDN with a Case Study of Ethernet LAN

Autor/inn/en	Ayadi, Mohamed Issam; Maizate, Abderrahim; Ouzzif, Mohammed; Mahmoudi, Charif
Titel	Deep Learning Forwarding in NDN with a Case Study of Ethernet LAN
Quelle	In: International Journal of Web-Based Learning and Teaching Technologies, 16 (2021) 1, S.1-9, Artikel 1 (9 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1548-1093
DOI	10.4018/IJWLTT.2021010101
Schlagwörter	Computer Networks; Artificial Intelligence; Mathematics; Models; Selective Dissemination of Information + Suchen Sie Ihr Suchwort? Computernetz; Computernetze; Kommunikationsnetz; Künstliche Intelligenz; Mathematik; Analogiemodell
Abstract	In this paper, the authors propose a novel forwarding strategy based on deep learning that can adaptively route interests/data packets through ethernet links without relying on the FIB table. The experiment was conducted as a proof of concept. They developed an approach and an algorithm that leverage existing intelligent forwarding approaches in order to build an NDN forwarder that can reduce forwarding cost in terms of prefix name lookup, and memory requirement in FIB simulation results showed that the approach is promising in terms of cross-validation score and prediction in ethernet LAN scenario. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01