典型文献
Network Traffic Clustering with QoS-Awareness
文献摘要：
Network traffic classification is essential in supporting network measurement and management.Many existing traffic classification approaches pro-vide application-level results regardless of the network quality of service(QoS)requirements.In practice,traffic flows from the same application may have ir-regular network behaviors that should be identified to various QoS classes for best network resource man-agement.To address the issues,we propose to con-duct traffic classification with two newly defined QoS-aware features,i.e.,inter-APP similarity and intra-APP diversity.The inter-APP similarity represents the close QoS association between the traffic flows that originate from the different Internet applications.The intra-APP diversity describes the QoS variety of the traffic even among those originated from the same Internet application.The core of performing the QoS-aware feature extraction is a Long-Short Term Memory neural network based Autoencoder(LSTM-AE).The QoS-aware features extracted by the en-coder part of the LSTM-AE are then clustered into the corresponding QoS classes.Real-life data from multiple applications are collected to evaluate the pro-posed QoS-aware network traffic classification ap-proach.The evaluation results demonstrate the effi-cacy of the extracted QoS-aware features in supporting the traffic classification,which can further contribute to future network measurement and management.
文献关键词：
中图分类号：
作者姓名：
Jielun Zhang;Fuhao Li;Feng Ye
作者机构：
Department of Electrical and Computer Engineering,University of Dayton,OH,45469,USA
文献出处：
引用格式：
[1]Jielun Zhang;Fuhao Li;Feng Ye-.Network Traffic Clustering with QoS-Awareness)[J].中国通信（英文版）,2022(03):202-214
A类：
B类：
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AB值：
0.485576
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