Traffic Prediction in Optical Networks Using Graph Convolutional Generative Adversarial Networks

被引：27
|
作者：
Vinchoff, C. [1 ]
Chung, N. [1 ]
Gordon, T. [1 ]
Lyford, L. [1 ]
Aibin, M. [1 ]
机构：
[1] British Columbia Inst Technol, 555 Seymour St, Vancouver, BC V6B 3H6, Canada
来源：
2020 22ND INTERNATIONAL CONFERENCE ON TRANSPARENT OPTICAL NETWORKS (ICTON 2020) | 2020年
关键词：
elastic optical networks; traffic prediction; deep learning; dynamic routing; GC-GAN;
D O I：
10.1109/icton51198.2020.9203477
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：
In this paper, we use a non-linear GCN-GAN model to predict burst events in the optical network. We model three distinct burst events as Plateau, Single-Burst and Double-Burst. Plateau represents the network under steady traffic, Single-Burst represents the network experiencing a rapid traffic spike followed by a steady decrease, and Double-Burst represents the network experiencing a rapid traffic spike followed by an unexpected greater traffic spike. We verify the model's effectiveness to predict these burst events in the real optical networks by comparing it to a basic LSTM, which has been shown to outperform other state-of-the-art models.
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页数：4
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