On-line SVM traffic classification

A wide range of traffic classification approaches has been proposed in the last few years by the scientific community. However, the development of complete classification architectures that work directly in real-time on high capacity links is limited. In this paper we present the implementation of a machine-learning technique (SVM), one of the most accurate but most computationally expensive mechanisms, on the CoMo project infrastructure. We show the computational time required to process different traffic traces and the optimization steps we adopted to improve the performance of the system and achieve real-time classification on high-speed links.