光通信技术2024，Vol.48Issue(3)：38-44,7.DOI:10.13921/j.cnki.issn1002-5561.2024.03.008
基于图神经网络的SDON性能预测模型
SDON performance prediction model based on graph neural network
摘要
Abstract
Network performance prediction is the key to achieving efficient network management of software defined optical net-works(SDON),but there is an urgent need for a network performance prediction model that can accurately predict key indicators at limited cost.A graph neural network-based SDON performance prediction model is proposed,which combines BiGRU and Self-Attention mechanisms to learn the complex relationships between network topology,routing,and traffic matrices,accurately estimating the packet delay,jitter,and packet loss rate of the source/destination in the network.This model can be applied to net-works that have not been encountered during training.The experimental results show that in different traffic model tests,the pro-posed model has a significant improvement in average absolute percentage error(MAPE)performance compared to the baseline model.关键词
图神经网络/网络性能预测/软件定义光网络/自注意力机制/光通信Key words
graph neural networks/network performance prediction/software-defined optical network/Self-Attention mecha-nisms/optical communication分类
信息技术与安全科学引用本文复制引用
王星宇,张慧,蔡安亮,沈建华..基于图神经网络的SDON性能预测模型[J].光通信技术,2024,48(3):38-44,7.基金项目
国家自然科学青年基金项目(62301284)资助 （62301284）
南京邮电大学企业委托研发重点课题(KH0020322072)资助. （KH0020322072）
