Advances in International Computer Science
Advances in International Computer Science. 2022; 2: (3) ; 10.12208/j. aics.20220054 .
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陆军装甲兵学院信息通信系 北京
*通讯作者： 于泽洋,单位：陆军装甲兵学院信息通信系 北京；
本文旨在为命名数据网络（Named Data Network，NDN）设计一种基于调整请求端兴趣包发送窗口的拥塞控制方法。文中首先介绍了目前NDN中拥塞控制方法的研究成果，对请求端拥塞控制和网内节点拥塞控制两种思路的优劣进行了分析，然后提出了一种基于机器学习流量预测的请求端拥塞控制方案设计，并在NDN网络仿真平台-ndnSIM上进行了实验验证。实验结果表明，与传统的请求端拥塞控制相比，新方法能够提高网络吞吐量、降低丢包率。
This paper aims to design a congestion control method for Named Data Network (NDN) based on adjusting the interest packet sending window of the requesting side.We first introduce researches on congestion control of NDN, analyze and compare two ideas that do congestion control on requesting side or metric nodes in the network.And then we put forward an end congestion control method based on machine learning traffic prediction, and do simulation experiment on the NDN simulation platform - ndnSIM .Experimental result shows that the proposed method can improve network throughput and reduce packet loss rate compared with traditional requesting side congestion control.
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