基于混合流量预测的虚拟网络拓扑重构方法

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多带宽资源。为了提高流量预测算法的精度与效率, 首先采用小波分解方法将流量数据分解为高频的细节时间序列和低频的近似时间序列, 然后利用基于粒子群优化的相空间重构方法, 对该时间序列进行特征提取构建训练样本。之后分别采用混沌模型对细节时间序列进行训练预测, 采用极限学习机(extreme learning machine, ELM)神经网络对近似时间序列进行训练预测。仿真结果表明, 所提的流量预测算法在保证预测精度的同时, 运行时间更短, 预测效率更高, 进而保证了拓扑重构方法可以节省更多的带宽资源。

Virtual network topology reconfiguration approach based on hybrid traffic prediction

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.