面向科研专网的链路流量预测模型  

Link Traffic Prediction Model for Scientific Research Network

作　　者：李菁菁[1,2] 杨校林 李俊 马彤宇[1] 尉书宾 LI Jing-Jing;YANG Xiao-Lin;LI Jun;MA Tong-Yu;WEI Shu-Bin(Computer Information Network Center,Chinese Academy of Sciences,Beijing 100190,China;University of Chinese Academy of Sciences,Beijing 100049,China)

机构地区：[1]中国科学院计算机网络信息中心,北京100190 [2]中国科学院大学,北京100049

出　　处：《计算机系统应用》2022年第2期48-56,共9页Computer Systems & Applications

摘　　要：当今科研活动已越来越依赖科研数据网络的高效传输,这对科研专网的链路资源规划和运行管理带来了更高要求.面向科研专网的实际需求建立链路流量预测模型能使网络运营者在SDN等先进控制转发技术辅助下更有效进行资源调度的快速决策.现有的预测方法未考虑当前网络流量更具多样化和更高复杂度的深层细粒度特征.通过改进LSTM模型,本文面向科研专网的管理需求提出了一种新型的链路流量预测模型,由自编码器AE、双向LSTM模型、单向LSTM模型和全连接层组成的AE-栈式混合LSTM模型,较大幅度提升了流量特征的提取能力,更好地挖掘不同时刻的数据特征之间前后依赖关系.本模型使用中国科技网CSTNet的全国骨干网真实生产环境中随机抽取的某一链路关联节点数据进行验证.实验结果证明本模型的预测结果符合流量真实变化趋势,且预测值与观测值之间的残差较小,能较好的拟合科研专网的现有流量.As scientific research is increasingly dependent on fast data transmission,the requirements for link resource planning and operation management of scientific research networks are more demanding.Considering the actual needs of scientific research networks,a good link traffic prediction model can help the network operators make fast decisions on link resource scheduling more effectively with the assistance of flexible network control technology such as SDN.The existing prediction model has ignored the current network traffic is more diversified and more complex in fine-grained features.This study proposes a new link traffic prediction model based on the improved LSTM model to meet the management needs of scientific research networks.Composed of AutoEncoder(AE),Bi-LSTM model,unidirectional LSTM model,and fully-connected layers,it can greatly improve the extraction ability of traffic features and better explore the dependent manners among data features at different time.The model is verified by using the associated node data of a link randomly selected from the real production environment of the national backbone network of Science and Technology Daily—CSTNet.The experimental results show that the prediction results of the model accord with the real change trend of traffic,and the residual between the predicted value and the observed value is small,which means the model can well fit the existing traffic of the scientific research network.

关 键 词：科研专网 网络管理 链路流量 预测模型 机器学习 流量预测 

分 类 号：TP393.06[自动化与计算机技术—计算机应用技术]