基于图神经网络的网络性能智能预测

李奕江; 叶会标; 谢仁华; 楼佳丽; 庄丹娜; 李传煌

doi:10.11959/j.issn.1000-0801.2022062

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基于图神经网络的网络性能智能预测

研究与开发 | 更新时间：2024-06-05

- 基于图神经网络的网络性能智能预测
- Intelligent prediction method of network performance based on graph neural network
- 电信科学 2022年38卷第3期页码：143-157
- 作者机构：
  
  1. 浙江工商大学信息与电子工程学院（萨塞克斯人工智能学院），浙江杭州 310018
  2. 中国电信股份有限公司浙江分公司，浙江杭州 310020
- 作者简介：
  
  [ "李奕江（1997- ），男，浙江工商大学信息与电子工程学院（萨塞克斯人工智能学院）硕士生，主要研究方向为新一代网络技术、人工智能应用" ]
  [ "叶会标（1973- ），男，中国电信股份有限公司浙江分公司云网监控维护中心核心网室主任，主要研究方向为中国电信4G、5G核心网、VoLTE网络的运营管理等" ]
  [ "谢仁华（1997- ），男，浙江工商大学信息与电子工程学院（萨塞克斯人工智能学院）硕士生，主要研究方向为新一代网络技术、人工智能应用" ]
  [ "楼佳丽（1998- ），女，浙江工商大学信息与电子工程学院（萨塞克斯人工智能学院）硕士生，主要研究方向为新一代网络技术、人工智能应用" ]
  [ "庄丹娜（1995- ），女，浙江工商大学信息与电子工程学院（萨塞克斯人工智能学院）硕士生，主要研究方向为新一代网络技术、人工智能应用" ]
  [ "李传煌（1980- ），男，博士，浙江工商大学信息与电子工程学院（萨塞克斯人工智能学院）教授、硕士生导师，主要研究方向为软件定义网络、开放可编程网络、边缘计算、人工智能应用" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61871468);国家自然科学基金资助项目;The National Natural Science Foundation of China(62111540270);浙江省新型网络标准与应用技术重点实验室项目(2013E10012);浙江省重点研发计划项目;The Key Research and Development Program of Zhejiang under Grant(2020C01079)
- DOI：10.11959/j.issn.1000-0801.2022062
  中图分类号： TP393
- 网络出版日期：2022-03，
  
  纸质出版日期：2022-03-20
- 稿件说明：
移动端阅览
李奕江, 叶会标, 谢仁华, 等. 基于图神经网络的网络性能智能预测[J]. 电信科学, 2022,38(3):143-157.

Yijiang LI, Huibiao YE, Renhua XIE, et al. Intelligent prediction method of network performance based on graph neural network[J]. Telecommunications science, 2022, 38(3): 143-157.
李奕江, 叶会标, 谢仁华, 等. 基于图神经网络的网络性能智能预测[J]. 电信科学, 2022,38(3):143-157. DOI： 10.11959/j.issn.1000-0801.2022062.

Yijiang LI, Huibiao YE, Renhua XIE, et al. Intelligent prediction method of network performance based on graph neural network[J]. Telecommunications science, 2022, 38(3): 143-157. DOI： 10.11959/j.issn.1000-0801.2022062.

摘要

传统网络性能预测技术存在网络状态获取不够全面及网络性能评估准确性欠佳等问题，利用图神经网络学习推理网络关系数据的特点，结合捕获的网络全局信息，提出了一种基于图神经网络的网络性能智能预测方法。通过网络系统抽象及网络性能建模，将复杂的网络信息转化为形式化的图数据进行描述，利用图空域卷积处理图网络节点的消息传递过程，实现网络信息之间的关系推理，研究了实现网络性能预测的图神经网络模型，提出了一种能处理流量矩阵、网络拓扑、路由策略、节点配置的图神经网络体系结构，最后通过实验论证了模型能更好地实现包括时延、抖动和丢包率的网络性能的准确预测。

Abstract

There are some problems in the traditional network performance prediction technology

such as incomplete network state acquisition and poor accuracy of network performance evaluation.Combined with the characteristics of graph neural network learning and reasoning network relational data and the captured global information of the network

on the basis of the current network performance prediction methods

an intelligent prediction method of network performance based on graph neural network was proposed.Aiming at the complex network information

through the research of network system abstraction and network performance modeling

the network information can be transformed into the graph space convolution was used to process the message passing process of graph network nodes to realize the relationship reasoning between network information.The graph neural network model for network performance prediction was studied

and a graph neural network architecture which could deal with traffic matrix

network topology

routing strategy and node configuration was proposed.Finally

the experiments show that the model can better achieve accurate prediction of the network performance including delay

jitter and packet loss rate.

关键词

Keywords

references

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