A Traffic Classification Method with Spectral Clustering in SDN
Peng Xiao, Na Liu, Yuanyuan Li, Ying Lü, Xiaojun Tang, Haiwen Wang, Mingxia Li · 2016
Traffic classification is becoming one of the major applications in the data center networks with a lot of cloud services. Recent works about software defined networking (SDN) have found new ways to manage data center networks. However, with the imbalance of the elephant and mice flows is sharpening, the accuracy and efficiency of traffic classification have become more and more important in SDN management. To address this issue, in this paper, we propose a traffic classification method that can deal with the traffic classification in SDN. Our method is based on spectral clustering and Software-Defined Networking (SDN). We propose a real-time flow extraction and representation method by scanning the flow tables in SDN controller. Then we cluster the flow data with spectral analysis. Extensive experiments on different settings have been performed, showing that our method is good at traffic classification with high detection rates and low overhead.