LAUSR

Internet video traffic exhibits considerable variation as new video services continue to emerge. Some videos require strict real-time performance, while others may aim for a minimal packet loss rate or sufficient bandwidth. Therefore, it is important to develop fine-grained classification mechanisms to realize effective resource management and quality of service (QoS) provisioning. However, the existing methods for classifying video traffic always suffer from two problems: payload inspection and feature selection. In this paper, we propose a novel method that uses fractal characteristics to achieve traffic classification at a fine-grained level. This method requires neither payload signatures nor statistical features. Through rigorous analysis, we prove the feasibility of employing fractal characteristics for video traffic classification and further develop a theoretical framework for the proposed scheme. For the specific scenario of video flow classification, we improve the theory of fractals in terms of estimated spectrum, core domain, segmentation, and threshold setting. The results of an extensive experimental study on several real-world video traffic datasets show that the classification accuracy of the proposed scheme is higher than that of existing methods.