A Framework for QoS-aware Traffic Classification Using Semi-supervised Machine Learning in SDNs
A Framework for QoS-aware Traffic Classification Using Semi-supervised Machine Learning in SDNs
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DOI：
10.1109/scc.2016.133
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发表时间：
2016-06
期刊：
影响因子：
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通讯作者：
Pu Wang;Shih-Chun Lin;Min Luo
中科院分区：
文献类型：
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作者：
Pu Wang;Shih-Chun Lin;Min Luo
提出了一种基于qos的软件定义网络流分类框架。与以往大多数流量分类工作中对特定应用的识别不同，本文的方法根据QoS需求将网络流量划分为不同的类别，为实现细粒度和QoS感知的流量工程提供了关键信息。该框架完全定位于网络控制器中，利用网络控制器优越的计算能力、全局可见性和固有的可编程性，实现实时、自适应、准确的流量分类。更具体地说，所提出的框架联合利用深度数据包检测（DPI）和半监督机器学习，从而实现准确的流量分类，同时要求网络控制器和SDN交换机之间的通信最少。基于真实互联网数据集的仿真结果表明，所提出的分类框架在分类精度和通信成本方面具有良好的性能。
In this paper, a QoS-aware traffic classification framework for software defined networks is proposed. Instead of identifying specific applications in most of the previous work of traffic classification, our approach classifies the network traffic into different classes according to the QoS requirements, which provide the crucial information to enable the fine-grained and QoS-aware traffic engineering. The proposed framework is fully located in the network controller so that the real-time, adaptive, and accurate traffic classification can be realized by exploiting the superior computation capacity, the global visibility, and the inherent programmability of the network controller. More specifically, the proposed framework jointly exploits deep packet inspection (DPI) and semi-supervised machine learning so that accurate traffic classification can be realized, while requiring minimal communications between the network controller and the SDN switches. Based on the real Internet data set, the simulation results show the proposed classification framework can provide good performance in terms of classification accuracy and communication costs.