光通信技术2024，Vol.48Issue(6)：34-39,6.DOI:10.13921/j.cnki.issn1002-5561.2024.06.007
基于自适应时空网络的SDN流量预测模型
SDN traffic prediction model based on adaptive spatiotemporal network
摘要
Abstract
To improve the accuracy of software defined networking(SDN)traffic prediction,an SDN traffic prediction model based on an adaptive spatiotemporal network is proposed.This model captures the spatial correlation of SDN traffic by using an adaptive graph convolutional neural network,captures temporal variation trends through gated recurrent units,and introduces an autoregressive module in response to the highly dynamic nature of SDN traffic.The experimental results show that the proposed SDN traffic prediction method can identify more traffic features compared to existing baseline models and demonstrates superior prediction performance.关键词
软件定义网络/流量预测/自适应图卷积网络/门控循环/时空相关性Key words
software defined network/traffic prediction/adaptive graph convolutional network/gated recurrent/spatiotemporal correlation分类
信息技术与安全科学引用本文复制引用
刘月,张慧,蔡安亮,沈建华..基于自适应时空网络的SDN流量预测模型[J].光通信技术,2024,48(6):34-39,6.基金项目
国家自然科学青年基金项目(批准号:62301284)资助. （批准号:62301284）
