SDN架构下的空间信息网络业务识别技术  

Spatial Information Network Business Identification Technology Under SDN Architecture

作　　者：潘成胜[1] 刘勇[1] 石怀峰[1,2] 杨力 PAN Chengsheng;LIU Yong;SHI Huaifeng;YANG Li(Key Laboratory of Communication and Network,School of Information Engineering,Dalian University,Dalian,Liaoning 116622, China;School of Automation,Nanjing University of Science and Technology,Nanjing 210094, China)

机构地区：[1]大连大学信息工程学院通信与网络重点实验室,辽宁大连116622 [2]南京理工大学自动化学院,南京210094

出　　处：《计算机工程》2019年第4期18-24,共7页Computer Engineering

基　　金：装备预研领域基金(6140449XX61001)

摘　　要：针对传统的离线业务流量识别方法消耗时间长、实时性差的问题,通过对空间信息网络管控和网络资源的高效编排,提出一种基于软件定义网络(SDN)架构的空间信息网络业务识别技术。运用OpenFlow协议在线收集业务流量,提取流中前5个数据包作为一条子流,在SDN控制器上实现基于机器学习的在线业务分类,同时给出一种具有噪声过滤功能的协同训练算法Dif-TriTraining。实验结果表明,与传统的Tri-Training算法相比,该算法能够有效提升业务识别的准确率。Aiming at the problem that the traditional offline business traffic identification method consumes a long time,and has poor real-time performance,through the management and control of spatial information network and the efficient arrangement of network resources,a business identification technology of spatial information network based on Software Defined Network (SDN) architecture is proposed.The OpenFlow protocol is used to collect business traffic online,and extract the first five data packets in the flow as a sub-flow,and implement online business classification based on machine learning on the SDN controller.At the same time,a collaborative training algorithm Dif-TriTraining with noise filtering function is presented.Experimental results show that compared with the traditional Tri-Training algorithm,the algorithm can effectively improve the accuracy of business identification.

关 键 词：软件定义 空间信息网络 业务识别 噪声过滤 协同训练 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]