Optical Network Traffic Prediction Based on Graph Convolutional Neural Networks

被引：0

作者：

Gui, Yihan ^{[1
]}

Wang, Danshi ^{[1
]}

Guan, Luyao ^{[1
]}

Zhang, Min ^{[1
]}

机构：

[1] Beijing Univ Posts & Telecommun, State Key Lab Informat Photon & Opt Commun, Beijing, Peoples R China

来源：

2020 OPTO-ELECTRONICS AND COMMUNICATIONS CONFERENCE (OECC 2020) | 2020年

基金：

中国国家自然科学基金;

关键词：

Traffic prediction; elastics optical networks; spatial-temporal dependence; graph convolutional network;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Understanding traffic patterns in largescale networks is of great importance for optical networks to implement intelligent management and adaptive adjustment. However, accurate traffic prediction in flexible optical networks is challenging because of the temporal and spatial autocorrelation of traffic. Spatial-temporal graph modeling is an effective approach to analyze the spatial relations and temporal trends of traffic in a system. We propose an efficient graph-based neural network named as the graph convolutional network with the gated recurrent unit (GCN-GRU). Based on a real-world optical networking traffic dataset, 98% accuracy for traffic prediction is achieved by GCN-GRU.

引用

页数：3

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