一种基于QoS特征的多媒体业务区分方法
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摘要
准确、高效的业务流识别和分类是保障多媒体通信端到端服务质量(Quality of Service,QoS)和执行相关网络操作的前提。但多媒体通信业务构成复杂,具有较严格的QoS约束,且存在包/流水平统计特征多样性,因此业务统计特征有效选取直接关系到识别和分类方法的有效性。针对流行的多媒体业务,分析了典型的业务特征,从业务QoS保证角度,选取区分特征,基于隐马尔可夫模型(Hidden Markov Model,HMM),对多媒体业务在QoS类上进行区分,实现简单,能以较小的空间复杂度较快地识别出多媒体业务流,有利于提高分类准确度。通过仿真验证了该方法的有效性。
The accurate and efficient identification/categorization of multimedia traffic is the premise of end-to-end QoS guarantees and corresponding network operations. The traffic structure of multimedia communications is very complex,and the most traffic of multimedia communications require strict QoS.Meanwhile,the statistical characteristics of multimedia traffic are diverse at packet/flow level.Therefore,it is vital to select appropriate traffic characteristics in packet/flow level for efficiently identifying multimedia traffic.This paper analyzes some typical flow characteristics for prevalent multimedia traffics,selects some differentiating characteristics from the point of view of QoS requirements,and designs a multimedia traffic QoS classification method based on HMM( Hidden Markov Model)to differentiate multimedia traffics according to QoS class.Finally,the simulation results are given to demonstrate the effectiveness of the proposed method.
The accurate and efficient identification/categorization of multimedia traffic is the premise of end-to-end QoS guarantees and corresponding network operations. The traffic structure of multimedia communications is very complex,and the most traffic of multimedia communications require strict QoS.Meanwhile,the statistical characteristics of multimedia traffic are diverse at packet/flow level.Therefore,it is vital to select appropriate traffic characteristics in packet/flow level for efficiently identifying multimedia traffic.This paper analyzes some typical flow characteristics for prevalent multimedia traffics,selects some differentiating characteristics from the point of view of QoS requirements,and designs a multimedia traffic QoS classification method based on HMM( Hidden Markov Model)to differentiate multimedia traffics according to QoS class.Finally,the simulation results are given to demonstrate the effectiveness of the proposed method.
引文
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[2] Zhang Jun,Chen Chao,Xiang Yang,et al.Internet Traffic Classification by Aggregating Correlated Naive Bayes Predictions[J]. IEEE Transactions on Information Forensics and Security,2013,8(1):5-15.
[3]许博,陈鸣,魏祥麟.基于隐马尔科夫模型的P2P流识别技术[J].通信学报,2012,33(6):55-63.
[4] Mu Xuefeng,Wu Wenjun. A Parallelized Network Traffic Classification Based on Hidden Markov Model[C]∥Proceedings2011 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery,Cyber C,2011:107-112.
[5] Khadimi A El,Lmater M A,Eddabbah M,et al. Packet Classification Using the Hidden Markov Model[C]∥2011 International Conference on Multimedia Computing and Systems,ICMCS’11,2011:1-5.
[6]张剑,钱宗珏,寿国础,等.在线聚类的网络流量识别[J].北京邮电大学学报,2011,34(1):103-106.
[7] Li Bing,Ma Maode,Jin Zhigang.A Vo IP Traffic Identification Scheme Based on Host and Flow Behavior Analysis[J].Journal of Network and Systems Management,2011,19(1):111-129.
[8] Gong Xiangyang,Wang Wendong,Cheng Shiduan.ERFC:An Enhanced Recursive Flow Classification Algorithm[J].Journal of Computer Science and Technology,2010,25(5):958-969.
[9] Kei Takeshita,Takeshi Kurosawa,Masayuki Tsujino,et al.Evaluation of HTTP Video Classification Method Using Flow Group Information[C]∥Proceedings of 2010 14th International Telecommunications Network Strategy and Planning Symposium,2010:1-6.
[10] Dario Rossi,Silvio Valenti.Fine-grained Traffic Classification with Netflow Data[C]∥IWCMC 2010Proceedings of the 6th International Wireless Communications and Mobile Computing Conference,2010:479-483.
[11] Jin Yu,Duffield Nick,Erman Jeffrey,et al.A Modular Machine Learning System for Flow-level Traffic Classification in Large Networks[J].ACM Transactions on Knowledge Discovery from Data,2012,6(1):1-34.
[12] Han Young-Tae,Park Hong-Shik.Game Traffic Classification Using Statistical Characteristics at the Transport Layer[J].ETRI Journal,2010,32(1):22-32.
[13] Liu haobin,He Jie,Guo Qiang. Real-time Identification Research of Unstructured P2P Multicast Video Streaming[J]. Proceedings 2012 4th International Conference on Multimedia and Security,2012:545-548.
[14] Giuseppe Aceto,Alberto Dainotti,Walter De Donato,et al.PortLoad:Taking the Best of Two Worlds in Traffic Classification[C]∥Proceedings INFOCOM 2010 IEEE Conference on Computer Communications Workshops,2010:1-5.
[15] Khakpour Amir R,Liu Alex X.An Information-theoretical Approach to High-speed Flow Nature Identification[J].IEEE/ACM Transactions on Networking,2013,21(4):1076-1089.
[16] Singh Hardeep.Performance Analysis of Unsupervised Machine Learning Techniques for Network Traffic Classification[C]∥International Conference on Advanced Computing and Communication Technologies,ACCT,v2015-April:401-404.
[17] Quoc D L,D’Alessandro V, Park B,et al. Scalable Network Traffic Classification Using Distributed Support Vector Machines[C]∥Proceedings 2015 IEEE 8th International Conference on Cloud Computing,CLOUD 2015:1008-1012.
[18] Ke Wenlong,Wang Yong,Lei Xiaochun,et al.Spark-based Feature Selection Algorithm of Network Traffic Classification[C]∥2017 13th International Conference on Computational Intelligence and Security,2017:140-144.
[19] Yuan Z,Wang C.An Improved Network Traffic Classification Algorithm Based on Hadoop Decision Tree[C]∥IEEE International Conference of Online Analysis and Computing Science(ICOACS),2016:53-56.
[20] Dong Y,Zhao J,Jin J.Novel Feature Selection and Classification of Internet Video Traffic Based on a Hierarchical Scheme[J].Computer Networks,2017,119:102-111.
[21] Shi H,Li H,Zhang D,et al.Efficient and Robust Feature Extraction and Selection for Traffic Classification[J].Computer Networks,2017,119:1-16.