Novel feature selection and classification of Internet video traffic based on a hierarchical scheme

Accurate traffic classification is critical for efficient network management and resources utilization. Different video traffics have different QoS (Quality of Service) requirements. To provide Internet video services with better QoS support, a fine grained classification scheme for network video traffic is proposed in this paper. Through extensive statistical analysis of typical video traffic flows with a consistency-based method, several new flow statistical features are extracted. They are found to be more effective in discriminating different video traffics, especially from the QoS perspective, than commonly used features available in the literature. A hierarchical k-Nearest Neighbor (kNN) classification algorithm is then developed based on the combinations of these statistical features. Experiments are performed to evaluate the effectiveness of the proposed method on a large scale real network video traffic data. The experimental results show that the proposed method outperforms existing methods applying commonly used flow statistical features.