DQN-AF: Deep Q-Network based Adaptive Forwarding Strategy for Named Data Networking

Autor:	de Sena, Ygor Amaral B. L., Dias, Kelvin Lopes, Zanchettin, Cleber
Rok vydání:	2020
Předmět:	Computer Science - Networking and Internet Architecture
Druh dokumentu:	Working Paper
Popis:	NDN has gained significant attention due to the appearance of several unforeseen design flaws that became evident with new communication scenarios. Among its many features, the two standard NDN forwarding strategies are not adaptive, causing performance degradation in several scenarios. This paper proposes an adaptive forwarding strategy based on deep reinforcement learning with Deep Q-Network, which analyzes the NDN router interface metrics without creating signaling overhead or harming the design principles from the NDN architecture, besides showing significant performance gains compared to the standard strategies. Comment: Accepted to be published in Proceedings of the 2020 IEEE Latin-American Conference on Communications (IEEE LATINCOM 2020), Nov 18-20, 2020
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2010.09897 Zobrazit plný text záznamu View this record from Arxiv