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

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

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Acknowledgements

This work is partially supported by the National Natural Science Foundation of China (NSFC) under Grants Nos. U1604155, U1404611 and 61602155, in part by the Program for Science & Technology Innovation Talents in the University of Henan Province under Grants No. 16HASTIT035, and in part by Henan Science and Technology Innovation Project under Grant Nos. 164200510007 and 174100510010, and in part by the Industry University Research Project of Henan Province under Grant No. 172107000005, and in part by the support program for young backbone teachers in Henan Province under Grant no. 2015GGJS-047.

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Correspondence to Mingchuan Zhang.

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Liu, T., Zhang, M., Zhu, J. et al. ACCP: adaptive congestion control protocol in named data networking based on deep learning. Neural Comput & Applic 31, 4675–4683 (2019). https://doi.org/10.1007/s00521-018-3408-2

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