Network Traffic Clustering with QoS-Awareness

作     者：Jielun Zhang Fuhao Li Feng Ye Jielun Zhang;Fuhao Li;Feng Ye

作者机构：Department of Electrical and Computer EngineeringUniversity of DaytonOH45469USA 

出 版 物：《China Communications》 (中国通信（英文版）)

年 卷 期：2022年第19卷第3期

页      面：202-214页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 081201[工学-计算机系统结构] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Network traffic clustering quality-of-service quality-of-experience deep learning 

摘      要：Network traffic classification is essential in supporting network measurement and *** existing traffic classification approaches provide application-level results regardless of the network quality of service(QoS)*** practice,traffic flows from the same application may have irregular network behaviors that should be identified to various QoS classes for best network resource *** address the issues,we propose to conduct traffic classification with two newly defined QoSaware features,i.e.,inter-APP similarity and intraAPP *** inter-APP similarity represents the close QoS association between the traffic flows that originate from the different Internet *** intra-APP diversity describes the QoS variety of the traffic even among those originated from the same Internet *** core of performing the QoS-aware feature extraction is a Long-Short Term Memory neural network based Autoencoder(LSTMAE).The QoS-aware features extracted by the encoder part of the LSTM-AE are then clustered into the corresponding QoS ***-life data from multiple applications are collected to evaluate the proposed QoS-aware network traffic classification *** evaluation results demonstrate the efficacy of the extracted QoS-aware features in supporting the traffic classification,which can further contribute to future network measurement and management.