| 基于深度学习的流量工程算法研究与应用 |
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| 引用本文： | 胡道允,齐进,陆钱春,李锋,房红强.基于深度学习的流量工程算法研究与应用[J].电信科学,2021,37(2):107-114. |
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| 作者姓名： | 胡道允 齐进 陆钱春 李锋 房红强 |
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| 作者单位： | 移动网络和移动多媒体技术国家重点实验室,广东深圳 518057;中国科学技术大学,安徽合肥 230026 |
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| 摘 要： | 随着5G网络的发展和应用,网络中的业务数量呈现出爆发式增长,网络中的带宽资源日趋紧张。为了提高网络资源利用率,并满足用户日益提高的业务服务质量要求,基于软件定义网络(SDN)提出了一种基于深度学习的流量工程算法(DL-TEA)。通过仿真证明该算法不仅能够实时地为业务计算一条高效的路径,同时还能够提升业务的QoS、网络资源利用率,降低网络阻塞率。
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| 关 键 词： | 软件定义网络 流量工程 深度学习 服务质量 |
Research and application of traffic engineering algorithm based on deep learning |
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| HU Daoyun,QI Jin,LU Qianchun,LI Feng,FANG Hongqiang.Research and application of traffic engineering algorithm based on deep learning[J].Telecommunications Science,2021,37(2):107-114. |
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| Authors: | HU Daoyun QI Jin LU Qianchun LI Feng FANG Hongqiang |
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| Affiliation: | (State Key Laboratory of Mobile Network and Mobile Multimedia Technology,Shenzhen 518057,China;University of Science and Technology of China,Hefei 230026,China) |
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| Abstract: | With the development and application of 5G network,the amount of traffic in network increased rapidly,which caused the lack of bandwidth resource.In order to improve the utilization of network resource and satisfy the critical user requirement for QoS(quality of service),a novel traffic engineering algorithm based on deep learning in SDN was proposed.At last,simulation results show that the proposed algorithm not only can calculate an efficient path for service in real time,but also can improve the QoS and the utilization of network resource,as well as reduce network congestion. |
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| Keywords: | SDN traffic engineering deep learning QoS |
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