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

Mohamed Issam Ayadi, Abderrahim Maizate, Mohammed Ouzzif, Charif Mahmoudi

Source Title: International Journal of Web-Based Learning and Teaching Technologies (IJWLTT)16(1)

ISSN: 1548-1093|EISSN: 1548-1107|EISBN13: 9781799859321|DOI: 10.4018/IJWLTT.2021010101

MLA

Ayadi, Mohamed Issam, et al. "Deep Learning Forwarding in NDN With a Case Study of Ethernet LAN." IJWLTT vol.16, no.1 2021: pp.1-9. http://doi.org/10.4018/IJWLTT.2021010101

APA

Ayadi, M. I., Maizate, A., Ouzzif, M., & Mahmoudi, C. (2021). Deep Learning Forwarding in NDN With a Case Study of Ethernet LAN. International Journal of Web-Based Learning and Teaching Technologies (IJWLTT), 16(1), 1-9. http://doi.org/10.4018/IJWLTT.2021010101

Chicago

Ayadi, Mohamed Issam, et al. "Deep Learning Forwarding in NDN With a Case Study of Ethernet LAN," International Journal of Web-Based Learning and Teaching Technologies (IJWLTT) 16, no.1: 1-9. http://doi.org/10.4018/IJWLTT.2021010101

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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.