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基于混合流量预测的虚拟网络拓扑重构方法
引用本文:史朝卫,孟相如,康巧燕,苏玉泽. 基于混合流量预测的虚拟网络拓扑重构方法[J]. 系统工程与电子技术, 2021, 43(5): 1382-1388. DOI: 10.12305/j.issn.1001-506X.2021.05.27
作者姓名:史朝卫  孟相如  康巧燕  苏玉泽
作者单位:1. 空军工程大学信息与导航学院, 陕西 西安 7100772. 中国人民解放军94303部队, 山东 潍坊 261000
基金项目:国家自然科学基金(61873277);陕西省重点研发计划(2020GY-026)资助课题。
摘    要:目前在构建虚拟网络时,为满足用户动态变化的带宽需求,虚拟网络控制平台通常把虚拟链路带宽设置为流量最大值,一定程度上造成了资源浪费.针对这一问题,提出一种基于混合流量预测的虚拟网络拓扑重构方法,利用基于参数优化选择的混合流量预测算法对下一周期的网络流量进行预测,根据流量预测结果进行拓扑重构,在避免出现乒乓效应的同时节省更...

关 键 词:流量预测  拓扑重构  小波分解  相空间重构  极限学习机
收稿时间:2020-06-18

Virtual network topology reconfiguration approach based on hybrid traffic prediction
SHI Chaowei,MENG Xiangru,KANG Qiaoyan,SU Yuze. Virtual network topology reconfiguration approach based on hybrid traffic prediction[J]. System Engineering and Electronics, 2021, 43(5): 1382-1388. DOI: 10.12305/j.issn.1001-506X.2021.05.27
Authors:SHI Chaowei  MENG Xiangru  KANG Qiaoyan  SU Yuze
Affiliation:1. College of Information and Navigation, Air Force Engineering University, Xi'an 710077, China2. Unit 94303 of the PLA, Weifang 261000, China
Abstract:The virtual network control plan usually set the virtual link bandwidth to the maximum value of traffic to meet user’s dynamically changing bandwidth requirements,which causes a waste of resources to a certain extent.In response to the problem,a virtual network topology reconfiguration approach based on hybrid traffic prediction is proposed.The hybrid traffic prediction algorithm based on parameter optimization selection is used to predict the network traffic in the next period,and the topology reconfiguration is performed according to the traffic prediction result,which avoids the ping-pong effect and saves more bandwidth resources.In order to improve the accuracy and efficiency of the traffic prediction algorithm,the wavelet decomposition method is first used to decompose the traffic data into high-frequency detailed time series and low-frequency approximate time series,and then the phase-space reconstruction method based on the particle swarm optimization is used to perform feature extraction on the time series to construct training samples.After that,the chaotic model is used to train and predict the detailed time series,and the extreme learning machine(ELM)neural network is used to train and predict the approximate time series.The simulation results show that the proposed traffic prediction algorithm guarantees the prediction accuracy,the running time is shorter,and the prediction efficiency is higher,thus ensuring that the topology reconfiguration approach can save more bandwidth resources.
Keywords:traffic prediction  topology reconfiguration  wavelet decomposition  phase-space reconstruction  extreme learning machine(ELM)
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