基于自编码器的分组光网络监测数据分析与优化方法研究
|
摘要
分组光网络作为用户业务的底层承载管道,需要实时对网络性能相关监测数据进行分析,并进行针对性的优化操作,以保障业务性能。针对以上管控需求,本文在分析已有数据分析算法的基础上,提出了基于LSTM+变分自编码器的分组光网络监测数据分析及优化框架,通过数据训练及可视化、特征分析及优化目标计算两个流程实现了对分组网性能数据的分析及优化目标计算,最后通过采集分组网时延数据作为试验对象,验证了所提出方法的有效性,可为人工智能在光网络监测及优化等管控领域的应用提供理论与试验指导。
As the underlying bearing pipe of user service, it's needed to analyze the network performance related monitoring data of packet optical network in real time and carry out targeted optimization operations to ensure the service performance. In view of the above control demands, based on the research of existing data analysis algorithms, this paper presented a LSTM+VAE based packet optical network monitoring data analysis and optimization framework, including data training and visualization, characteristics analysis and optimization target calculation. At last, by collecting packet optical network delay data as experimental data, the effectiveness of the proposed method is verified. This research can also provide theoretical and experimental guidance for applyingArtificial Intelligence into the field of optical network management and control such as monitoring and optimization.
As the underlying bearing pipe of user service, it's needed to analyze the network performance related monitoring data of packet optical network in real time and carry out targeted optimization operations to ensure the service performance. In view of the above control demands, based on the research of existing data analysis algorithms, this paper presented a LSTM+VAE based packet optical network monitoring data analysis and optimization framework, including data training and visualization, characteristics analysis and optimization target calculation. At last, by collecting packet optical network delay data as experimental data, the effectiveness of the proposed method is verified. This research can also provide theoretical and experimental guidance for applyingArtificial Intelligence into the field of optical network management and control such as monitoring and optimization.
引文
[1]Wong J A H A.Algorithm AS 136:A K-Means clustering algorithm[J].Journal of the Royal Statistical Society.Series C(Applied Statistics),1979,28(1):100-108.
[2]Ester M,Kriegel H P,J?rg Sander,et al.A density-based algorithm for discovering clusters in large spatial databases with noise[C].Proceedings of the Second International Conference on Knowledge Discovery and Data Mining,AAAI Press,1996.
[3]Manevitz L M,Yousef M.One--class SVMs for document classification[J].Journal of Machine Learning Research,2002,2(1):139-154.
[4]Jing Wang,Jian Tang,Zhiyuan Xu,Yanzhi Wang,Guoliang Xue,Xing Zhang,and Dejun Yang.Spatiotemporal modeling and prediction in cellular networks:a big data enabled deep learning approach[C].In INFOCOM-36th Annual IEEE International Conference on Computer Communications,2017.
[2]Ester M,Kriegel H P,J?rg Sander,et al.A density-based algorithm for discovering clusters in large spatial databases with noise[C].Proceedings of the Second International Conference on Knowledge Discovery and Data Mining,AAAI Press,1996.
[3]Manevitz L M,Yousef M.One--class SVMs for document classification[J].Journal of Machine Learning Research,2002,2(1):139-154.
[4]Jing Wang,Jian Tang,Zhiyuan Xu,Yanzhi Wang,Guoliang Xue,Xing Zhang,and Dejun Yang.Spatiotemporal modeling and prediction in cellular networks:a big data enabled deep learning approach[C].In INFOCOM-36th Annual IEEE International Conference on Computer Communications,2017.