[1]李鹏飞,邵维专.深度学习在 SDN 中的应用研究[J].计算机技术与发展,2019,29(01):1-5.[doi:10.3969/j.issn.1673-629X.2019.01.001]
　LI Peng-fei,SHAO Wei-zhuan.Research on Application of Deep Learning in SDN[J].,2019,29(01):1-5.[doi:10.3969/j.issn.1673-629X.2019.01.001]
点击复制

深度学习在 SDN 中的应用研究()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
29
期数:
2019年01期
页码:
1-5
栏目:
智能、算法、系统工程
出版日期:
2019-01-10

文章信息/Info

Title:
Research on Application of Deep Learning in SDN
文章编号:
1673-629X(2019)01-0001-05
作者:
李鹏飞; 邵维专
四川大学 计算机学院,四川 成都 610065
Author(s):
LI Peng-fei; SHAO Wei-zhuan
School of Computer,Sichuan University,Chengdu 610065,China
关键词:
深度学习; 软件定义网络; 应用; 研究
Keywords:
deep learning; software defined networking; application; research
分类号:
TP393
DOI:
10.3969/j.issn.1673-629X.2019.01.001
摘要:
软件定义网络作为一种最新网络架构,可通过软件编程的方式定义和控制网络,其控制平面和转发平面分离及开放性可编程的特点,为新型互联网体系结构研究提供了新的实验途径,也极大地推动了下一代互联网的发展。 深度学习相对于传统的机器学习有很多优点,深度学习能够发现多层特征,并能够将高层特征表示成更抽象的数据特征。 深度学习网络模型因为具有多个隐藏层而具有很强的特征学习能力,相对于机器学习模型来说具有很大的进步。 随着深度学习的快速发展,有必要在软件定义网络中引入深度学习,推进软件定义网络的进一步发展。 从架构、数据源、快速特征提取、深度学习算法选择和分析深度学习在 SDN 中的现有应用五个方面来说明深度学习在 SDN 中的应用。
Abstract:
Software defined networking (SDN),as an innovational network framework,can enable programmers to control and define the networks by software programming. Its features of separation of control plane and forwarding plane and open programmable have provided a new experimental approach for the study of new Internet architecture and greatly promoted the development of the next generation of Internet. Deep learning has many advantages over traditional machine learning,and it can discover multilayer features and represent higherlevel features as more abstract data features. The deep learning network model has a strong feature learning ability due to its multiple hidden layers,which is a great improvement compared with the machine learning model. With the rapid development of deep learning,it is necessary to introduce deep learning into the SDN which is promoted in further development. The application of deep learning in SDN is illustrated from five aspects including architecture,data source,rapid feature extraction,deep learning algorithm selection and analysis of existing applications of deep learning in SDN

相似文献/References:

[1]郭文刚. 基于SDN的大型企业网络研究[J].计算机技术与发展,2014,24(08):179.
　GUO Wen-gang. Research on Large Enterprise Network Based on SDN[J].,2014,24(01):179.
[2]孔祥彬,沈苏彬,李 莉.一种基于 SDN 网络的 QoS 路由选择方案[J].计算机技术与发展,2018,28(02):102.[doi:10．3969/j．issn．1673－629X．2018．02．023]
　KONG Xiangbin,SHEN Subin,LI Li.A QoS Ｒouting Scheme Based on Software－defined Networking[J].,2018,28(01):102.[doi:10．3969/j．issn．1673－629X．2018．02．023]
[3]季一木,谈海宇,孙延鹏,等. 基于Openflow的Flash P2P流媒体传输协议研究[J].计算机技术与发展,2015,25(11):82.
　JI Yi-mu,TAN Hai-yu,SUN Yan-peng,et al. Research on Flash P2P Streaming Media Transmission Protocol Based on Openflow[J].,2015,25(01):82.
[4]王莉. 面向QoE驱动的软件定义网络业务流控制模型[J].计算机技术与发展,2015,25(11):125.
　WANG Li. Model of Software Defined Network Service-flow Control to QoE-driven[J].,2015,25(01):125.
[5]陈强锐,谢世朋.基于深度学习的肺部肿瘤检测方法[J].计算机技术与发展,2018,28(04):201.[doi:10.3969/ j. issn.1673-629X.2018.04.043]
　CHEN Qiang-rui,XIE Shi-peng.Lung Cancer Detection Method Based on Deep Learning[J].,2018,28(01):201.[doi:10.3969/ j. issn.1673-629X.2018.04.043]
[6]施泽浩,赵启军.基于全卷积网络的目标检测算法[J].计算机技术与发展,2018,28(05):55.[doi:10.3969/j.issn.1673－629X.2018.05.013]
　SHI Ze-hao,ZHAO Qi-jun.Object Detection Algorithm Based on Fully Convolutional Neural Network[J].,2018,28(01):55.[doi:10.3969/j.issn.1673－629X.2018.05.013]
[7]黄法秀,张世杰,吴志红,等.数据增广下的人脸识别研究[J].计算机技术与发展,2020,30(03):67.[doi:10. 3969 / j. issn. 1673-629X. 2020. 03. 013]
　HUANG Fa-xiu,ZHANG Shi-jie,WU Zhi-hong,et al.Research on Face Recognition Based on Data Augmentation[J].,2020,30(01):67.[doi:10. 3969 / j. issn. 1673-629X. 2020. 03. 013]
[8]孙茂鑫,钱红燕. SDN网络环境下的MPTCP的移动切换机制[J].计算机技术与发展,2016,26(06):11.
　SUN Mao-xin,QIAN Hong-yan. Mobile Handover Mechanism Based on MPTCP in SDN Environment[J].,2016,26(01):11.
[9]陈浩翔,蔡建明,刘铿然,等. 手写数字深度特征学习与识别[J].计算机技术与发展,2016,26(07):19.
　CHEN Hao-xiang,CAI Jian-ming,LIU Keng-ran,et al. Deep Learning and Recognition of Handwritten Numeral Features[J].,2016,26(01):19.
[10]高翔,陈志,岳文静,等.基于视频场景深度学习的人物语义识别模型[J].计算机技术与发展,2018,28(06):53.[doi:10.3969/ j. issn.1673-629X.2018.06.012]
　GAO Xiang,CHEN Zhi,YUE Wen-jing,et al.Human Semantic Recognition Model Based on Video Scene Deep Learning[J].,2018,28(01):53.[doi:10.3969/ j. issn.1673-629X.2018.06.012]

更新日期/Last Update: 2019-01-10