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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 9/2019

05.03.2018 | S.I. : Emergence in Human-like Intelligence towards Cyber-Physical Systems

ACCP: adaptive congestion control protocol in named data networking based on deep learning

verfasst von: Tingting Liu, Mingchuan Zhang, Junlong Zhu, Ruijuan Zheng, Ruoshui Liu, Qingtao Wu

Erschienen in: Neural Computing and Applications | Ausgabe 9/2019

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Abstract

Named data networking (NDN) is a novel network architecture which adopts a receiver-driven transport approach. However, NDN is the name-based routing and source uncontrollability, and network congestion is inevitable. In this paper, we propose an adaptive congestion control protocol (ACCP) which is divided into two phase to control network congestion before affecting network performance. In the first phase, we employ the time series prediction model based on deep learning to predict the source of congestion for each node. In the second phase, we estimate the level of network congestion by the average queue length based on the outcomes of first phase in each router and explicitly return it back to receiver, and then the receiver adjusts sending rate of Interest packets to realize congestion control. Simulation experiment results show that our proposed ACCP scheme has better performance than ICP and CHoPCoP in terms of the high utilization and minimal packet drop in a multi-source/multi-path environment.
Vorheriger Artikel Open-circuit fault detection for three-phase inverter based on backpropagation neural network
Nächster Artikel A novel collaborative filtering algorithm of machine learning by integrating restricted Boltzmann machine and trust information

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Metadaten
Titel
ACCP: adaptive congestion control protocol in named data networking based on deep learning
verfasst von
Tingting Liu
Mingchuan Zhang
Junlong Zhu
Ruijuan Zheng
Ruoshui Liu
Qingtao Wu
Publikationsdatum
05.03.2018
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 9/2019
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-018-3408-2

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