Research and application of traffic engineering algorithm based on deep learning

Daoyun HU; Jin QI; Qianchun LU; Feng LI; Hongqiang FANG

doi:10.11959/j.issn.1000-0801.2021027

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Research and application of traffic engineering algorithm based on deep learning

Research and Development | 更新时间：2024-06-05

- Research and application of traffic engineering algorithm based on deep learning
- Telecommunications Science Vol. 37, Issue 2, Pages: 107-114(2021)
- 作者机构：
  
  1. 移动网络和移动多媒体技术国家重点实验室，广东深圳 518057
  2. 中国科学技术大学，安徽合肥 230026
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2021027
  CLC： TP393
- Published Online：2021-02，
  
  Published：20 February 2021
- 稿件说明：
移动端阅览
Daoyun HU, Jin QI, Qianchun LU, et al. Research and application of traffic engineering algorithm based on deep learning[J]. Telecommunications science, 2021, 37(2): 107-114.
DOI：

Daoyun HU, Jin QI, Qianchun LU, et al. Research and application of traffic engineering algorithm based on deep learning[J]. Telecommunications science, 2021, 37(2): 107-114. DOI： 10.11959/j.issn.1000-0801.2021027.

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。

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.

关键词

Keywords

references

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