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: [email protected]; [email protected]; [email protected]; [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. The as significant features to distinguish different videos. Literature results show that video traffic classification with multi-fractals, can effectively mitigate the defects of statistical features, and [9] also designated packet size as features to use when achieve a superior performance. analyzing a large volume of video flows, such as HTTP video flows generated by YouTube. With machine learning and the Index Terms—classification; feature; multi-fractals; video Gaussian mixture model, HTTP video flows are eventually traffic. divided into five categories: cartoons, news, advertising, music and sports. However, some issues are exposed with in-depth research by I. INTRODUCTION investigators: 1) Sometimes, a large number of features can Video has become one of the most popular network services identify only a few categories. In Literature [10], more than 12 with the innovation of 4G and 5G technologies and is growing features were extracted to identify HTTP flows with the method rapidly on a tremendous scale at present [1]. There are so many of fast feature selection algorithm (FFSA). 2) The features, varieties of video traffic that it is necessary to build an effective which can effectively identify the previous set of flow samples, classification system to execute resources management and to generally may not do so for the next set, so those features need provide technical support to guarantee quality of service (QoS) to be properly adjusted against actual situations, which will [2]. For example, video conferencing and telemedicine systems lead to a series of problems. For example, Literature [11] tried have critical requirement of real-time, and any unexpected to classify flows by KNN(k-Nearest Neighbor). However, its K delay may result in considerable economic loss and decision factor, which determines the number of categories, can only be errors [3]. Therefore, it is imminent to classify video traffic, and manually adjusted as features are changed. 3) Features it has become a research focus in the fields of multimedia extracted from given flow samples are destined to be communications and network traffic classification [4]. ineffective for unknown flows. 4) Generally, better In the current research on traffic classification [5], traffic is performance can be achieved by adding more features; firstly divided into flows, which is defined as a set of packages however, computation and storage costs will grow that have the same properties, and usually described by a exponentially [12]. Moreover, there is evidence of a strong five-tuple: <Src IP, Dest IP, Src Port, Dest Port, Protocol > [6]. correlation between the features, and more features will lead to Then flows are classified into video conference, telemedicine more redundancy, which will greatly reduce the classification system, electronic commerce, and so on. That is, classification accuracy and efficiency [13]. 5) A large number of test samples of video traffic is actually classification of flows. are needed to extract effective features based on statistics, and The existing methods of classifying video flows now are the process is time-consuming [9]. mostly focusing on statistical methods, and the process can be The above statistical features has obstructed the generally described as follows: first, flow samples are observed classification of video flows from developing. So it is necessary and analyzed, and then useful features, such as flow size, to have further research on video flows and to explore new transmission rate, duration time, packet number and average methods. Accordingly, multi-fractals are introduced in this size of packets, are extracted based on statistics, and we call it paper to cope with the issue of statistical features. the statistical features later on. Consequently, flows can be The major contributions of this paper are summarized as classified by these features accompanied with support vector follows: 1) based on multi-fractals, we propose a novel classification method using fractal characteristics to classify According to (1) and (2), if the sequence of k [X ] video flows; 2) the proposed method does not require statistical N/m features, so as to avoid the long-term process of extracting satisfies： m features based on statistics and other limitations caused by n k (4) N lim ln[( N [X ])] nN statistical features; 3) multi-fractals are theoretically improved m i N m m in this paper, which would be helpful for the theory to be used then the multi-fractal spectrum can be described as： in other research fields. The rest of the paper is organized as follows. Section II 1 n f ( ) lim lim ln N( ) n (5) introduces fractal concepts for video flow classification. G N α ( , ) 0 n n N Section III presents a detailed description of our method based m m on multi-fractals. Section IV contains a series of experiments where fG(α) is the multi-fractal spectrum of flows sequence. and comparisons. Finally, Section V concludes this paper with The multi-fractal spectrum of flow sequence, fG(α), which a discussion of the future work. can describe the complex characteristics of flows, are expected to distinguish different video flows. II. BACKGROUND KNOWLEDGE Our proposed method does not rely on statistical features and Multi-fractals involve an infinite set of singular exponents classifies flows without feature extractions, which lead to a which describe the variability of random, non-stable data. It is superior performance in a real network with high dynamic useful to explore ordering rules from a state of disorder and variation. reveal specific principles from complex, broken and chaotic phenomena. III. FRACTAL CLASSIFICATION METHOD According to multi-fractal theory, different regions of the In this section, we present a detailed description of our same fractal material generally have the same fractal classification method based on multi-fractals. characteristics, and these characteristics describe the complex relationship in multiple dimensions, so fractal characteristics A. The estimated fractal spectrum are expected to distinguish materials. For example, in In the field of multi-fractals, the multi-fractal spectrum fG(α) Literature [14] fingerprints were identified by fractal theory. is the mathematical representation of complex fractal Leland and others also introduced multi-fractals to analyze characteristics, which is difficult to accurately calculate by (5). network traffic [15], such as predicting traffic, controlling Generally, the estimated value of fG(α) can be relatively easy traffic congestion, investigating traffic variability, etc. [16]. to obtain with the method of numerical analysis[15], and Just as fractal characteristics of materials can help to moreover, flows are in a discrete sequence after sampling in a distinguish them, we expect fractal characteristics of traffic can computer, so the estimated spectrum based on Legendre be used to classify video flows. transformation is explored to model the multi-fractal spectrum According to multi-fractal theory, μk (ε), which represents of flows. the measurement of unit k of the scaling value of ε, satisfies the Normalize flows sequence {X (k;m,N)} according to (3): relation: k N [X ] k ( ) (1) k m (6) k [X ] = N j [X ] where is the Holder exponent or the singularity exponent. m N j The objective space is divided into several subsets m Define partition function： according to α k, and, if each subset has the fractal N q characteristics of f (αk), then fractal spectrum fG(α) can be m m described as: (m(k 1)j ) S m(q) N [X ] (7) f ( ) N( ) G (2) k 1 j 1 m where N(α) represents the number of subsets that have the Define scaling function： value of α. 1 Note that flow {X(t)} is a stochastic process, which is (q) lim log S m(q) (8) sampled in the computer and converted into a discrete sequence. m m An increment process of flow {X(t)} has the same sampling According to (4) and (7), the partition function of Sm(q) can process and the discrete sequence can be described as (defining be defined as： n n nq k N as resolution, and N=2 ): 2 1 α N N N m n q - - S (q) 2 (2 ) k m m m [X ] = X((k + 1)2 )-X(k2 ) (3) (9) N k 0 m n(q fG ( )) where k=0,1,...2n-1, m=0,1,...n.