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Internet Multimedia Traffic Classification from Qos Perspective Using Semi-Supervised Dictionary Learning Models

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Internet Multimedia Traffic Classification from Qos Perspective Using Semi-Supervised Dictionary Learning Models NETWORKS & SECURITY Internet Multimedia Traffic Classification from QoS Perspective Using Semi-supervised Dictionary Learning Models Zaijian Wang1,*, Yuning Dong2, Shiwen Mao3, Xinheng Wang 1 College of Physics and Electronic Information, Anhui Normal University, Wuhu, 241000 China 2 College of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, 210003 China 3 Department of Electrical and Computer Engineering, Auburn University, Auburn, AL 36849-5201 USA 4 School of Computing, University of the West of Scotland, Paisley, PA1 2BE, UK * The corresponding author, email: [email protected] Aabstract: To address the issue of fine- sifier. Our experimental results demonstrate grained classification of Internet multimedia the feasibility of the proposed classification traffic from a Quality of Service (QoS) per- method. spective with a suitable granularity, this paper Keywords: dictionary learning; traffic clas- defines a new set of QoS classes and presents sication; multimedia traffic; K-singular value a modified K-Singular Value Decomposition decomposition; quality of service (K-SVD) method for multimedia identifi- cation. After analyzing several instances of I. INTRODUCTION typical Internet multimedia traffic captured in a campus network, this paper defines a new With the development of various Internet mul- set of QoS classes according to the difference timedia applications, cross-layer optimization in downstream/upstream rates and proposes a of network resource allocation for enhanced modified K-SVD method that can automatical- user experiences has attracted considerable ly search for underlying structural patterns in interests in the research community [1-8]. the QoS characteristic space. We define bag- On the other hand, Internet Service Pro- QoS-words as the set of specific QoS local viders (ISPs) need to consider different Qual- patterns, which can be expressed by core QoS ity-of-Service (QoS) requirements for data, characteristics. After the dictionary is con- voice and video applications because users structed with an excess quantity of bag-QoS- need better, more personalized services. How- words, Locality Constrained Feature Coding ever, due to the heterogeneity of the definition (LCFC) features of QoS classes are extracted. of QoS/service classes with different levels By associating a set of characteristics with a of granularity for various types of multimedia percentage of error, an objective function is traffic, ISPs still have difficulties guaranteeing formulated. In accordance with the modified the end-to-end QoS of multimedia services by K-SVD, Internet multimedia traffic can be allocating proper network resources, especially Received: Feb. 24,2017 classified into a corresponding QoS class with when they target high-bandwidth applications Revised: Jun. 8, 2017 a linear Support Vector Machines (SVM) clas- such as video streaming [9-12]. Granularity, Editor: Youping Zhao 202 China Communications • September 2017 in particular, should not be used when classi- cepts in various major benchmarks [17], clas- fying, existing types of the multimedia traffic sification performance should be improved by The authors intend to with new types of multimedia traffic. using dictionary learning models in the QoS address the problem For example, video traffic can be further domain. of effective classifying classified as Peer-to-Peer (P2P)-based or In this paper, we define seven QoS catego- multimedia traffic with a suitable gran- non-P2P-based, each with different dynamic ries with newly discovered features and pro- ularity, and presented characteristics. To efficiently utilize limited pose a characteristic-based method which will a modified K-SVD network resources, it is important to classify classify Internet multimedia traffic by utilizing Internet multimedia Internet multimedia traffic using a finer granu- a modified K-Singular Value Decomposition traffic identification larity, so that Internet multimedia services can (K-SVD) method. The K-SVD will learn the framework based on be improved with better end-to-end QoS guar- dictionary so that it can classify various Inter- the concept of QFAg in this paper. antee by ISPs. In order to effectively provide net multimedia traffic with appropriate differ- end-to-end QoS in heterogeneous networks, entiating granularity of multimedia services by authors in [12] emplyed an Application Ser- considering QoS requirements. By analyzing vice Map (ASM) to classify traffic, while traf- multimedia flows in a large-scale, real world fic was divided into eight Unified Communi- network, we find that the new QoS-related cation (UC) classes in [13]. In addition, online features correlate with the bidirectional rates allocation of communication and computation of different streams of Internet multimedia resources for different real-time multimedia traffic. After investigating the sparse nature of services was considered in [14] to provide an the QoS of multimedia streams, we extract the optimal service to users. training dictionary from training samples, uti- In order to offer uninterrupted services lize a modified K-SVD to learn the dictionary, when using a mobile Internet, a Flow Aggre- and propose a characteristic-based method to gation (FAg) concept was proposed in our pre- classify multimedia traffic. Specifically, our vious work [15], which involved aggregating main contributions in this paper are summa- traffic flows with similar QoS requirements rized as follows: into a FAg. We show that the FAg may act as a 1) by extensive statistical analysis of traffic bridge to help different network/QoS domains flow data collected from a real world net- along the transmission path understand/trans- work, we find that downstream/upstream late QoS requirements with a unified view. rates are suitable for differentiating Internet However, how to utilize the FAg in an effec- multimedia traffic (e.g., video and game tive way is still an unresolved issue. In this traffic) from a QoS perspective; paper we try to tackle this problem by using 2) based on the sparse property of the QoS an appropriate, differentiating granularity of characteristics, we show that the current multimedia services from a QoS perspective types of Internet multimedia traffic (e.g., to achieve the FAg objective. video and game traffic) can be classified To achieve this goal, it is necessary to clas- into seven QoS based FAg (QFAg) catego- sify Internet multimedia traffic into proper ries; granularity categories based on QoS related 3) we propose a modified, K-SVD-based characteristics. Most existing traffic classifica- Internet multimedia traffic classification tion methods focus on a specific application/ framework based on the concept of QFAg, protocol but not on QoS related characteristics which improves the classification perfor- [16]. However, traffic belonging to a single mance significantly by employing a unified QoS class may be from different application objective function incorporated with QoS types and generated by different protocols. features, for which the K-SVD algorithm Considering that the Bag-of-Words (BoW) can be utilized to efficiently solve the clas- method has been proven to be the most effi- sification problem. cient classification scheme for individual con- The remainder of this paper is organized as China Communications • September 2017 203 follows. Section II presents related work in the Inter-Arrival Time (IAT), delay, jitter, and literature. Section III introduces the setup of packet loss [18]. These characteristics are not our data collection from a real world network suitable for effectively creating the QoS class- and presents the dataset. Section IV analyzes es because they cannot reflect the differences the related typical QoS characteristics, selects within the QoS classes. For example, packet 1 http://www.cntv.cn/. new QoS characteristics for differentiating size, frequently described in the literature, is 2 http://www.bittorrent. Internet multimedia traffic streams, and pres- an important characteristic, but cannot be used com/. ents the definition of the QoS class for Internet to make a distinction between Closed Circuit 3 http://skype.gmw.cn/. multimedia traffic. Section V develops a QoS Television (CCTV1) web video and BitTorrent2 4 http://iptv.cntv.cn/. class recognition framework based on modi- video. Similarly, IAT also needs to be further fied K-SVD for Internet multimedia traffic and processed before it can be applied to differen- Section VI analyzes its complexity. Experi- tiate traffic types [19]. mental results are given in Section VII, and To contrast Skype3 with IPTV4, authors Section VIII concludes the paper. in [20]. studied the corresponding statisti- cal characteristics of IAT and packet size by II. RELATED WORK using an interactive mode and packet prop- erties. Researchers in [21] showed that IAT The International Telecommunication Union and packet size may affect jitter, packet loss, (ITU), the Internet Engineering Task Force and bandwidth. IAT, packet loss and jitter for (IETF), and some other standard organi- Skype in UMTS were analyzed in [22]. The zations, as well as network providers have results indicated that QoS characteristics have stipulated a large number of typical QoS char- different distributions in different flow direc- acteristics of Internet multimedia traffic [11]. tions. Researchers in [23] indicated that, in For example, ITU-T Recommendation G.1010 online gaming, there were obvious differenc- primarily adopts
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