Classification of Internet Video Traffic Using multi-Fractals Pingping TANG1,2, Yuning DONG1, Zaijian WANG 2, Lingyun YANG 1 1. College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China. 210003 2. College of Physics and Electronic Information, Anhui Normal University, Wuhu, China. 2410002 e-mail:
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[email protected] Abstract—Video traffic is booming in the Internet, and the machine (SVM), decision tree, or neural network, etc. types of video traffic are numerous. So it is necessary and The above methods have achieved some preliminary results. imminent to effectively classify video traffic. The existing methods For example, Literature [7] was concentrating on classifying of classifying video traffic depend heavily on extracted features, P2P-TV with SVM, in which the extracted feature is the which are statistically accessed from given samples, and thus are destined to be ineffective for other types of video traffic. Therefore, number of packets during a given period of time and then in this paper, we propose a novel classification method based on P2P-TV is divided into four categories: PPlive, Sop Cast, the theory of multi-fractals, and it relies on fractal characteristics TVAnts and Joost. Literature [8] provided deeper insight into rather than statistical features to classify video traffic. A number Skype flows by utilizing active and passive technology; then bit of experiments are performed to demonstrate the feasibility of the rate, interval time of packets and size of packets are designated proposed method and its adaptability to new environments.