使用改进K-SVD的网络多媒体业务QoS类识别

王再见; 董育宁; 汤萍萍; 杨凌云; 张晖

doi:10.11999/JEIT170133

使用改进K-SVD的网络多媒体业务QoS类识别

doi: 10.11999/JEIT170133

基金项目:

国家自然科学基金(61401004, 61271233, 61471203), 2016年安徽省高校领军人才引进与培育计划项目(gxfxZD2016013)，安徽师范大学博士科研启动基金(2016XJJ129)

计量
- 文章访问数: 853
- HTML全文浏览量: 108
- PDF下载量: 191
- 被引次数: 0
出版历程
- 收稿日期: 2017-02-17
- 修回日期: 2017-10-01
- 刊出日期: 2017-12-19

Network Multimedia QoS Class Recognition Based on Improved K-SVD

(College of Physics and Electronic Information, Anhui Normal University, Wuhu 241000, China)

Funds:

The National Natural Science Foundation of China (61401004, 61271233, 61471203), The Plan of Introduction and Cultivation of University Leading Talents in Anhui (gxfxZD2016013), The Startup Project of Anhui Normal University Doctor Scientific Research (2016XJJ129)

摘要

摘要: 该文基于网络多媒体业务QoS(Quality of Service)特征特点，提出网络业务QoS类识别算法。探索了新的多媒体业务QoS类划分模式，在QoS分类的基础上，可以通过将具有相同或相似QoS需求特征的业务流聚集生成聚集流。聚集流划分使用较少的QoS特征，借助聚集流可以在合理的粒度上区分多媒体业务。该文从QoS特征出发分析了聚集流识别的特点，利用网络多媒体业务典型QoS特征的稀疏性，使用改进K-SVD(Kernel Singular Value Decomposition)进行字典学习，实现网络多媒体业务QoS类识别。实验结果表明，该文算法比现有方法具有更高的QoS类识别准确性。
- 异构网络 /
- 网络多媒体 /
- QoS /
- 稀疏表示 /
- 业务流识别 /
- 字典训练
Abstract: According to QoS characteristics of network multimedia service, this paper proposes a algorithm of network multimedia QoS class recognition. This paper studies new multimedia traffic QoS class division mode. According to new QoS classes defined, Flow Aggregation (FA) can be formed by gathering multimedia traffic flows with similar QoS characteristics. Network multimedia QoS class recognition prefers fewer QoS features by FA, and it is possible to divide network multimedia traffics in suitable granularity based on FA. This paper analyzes the property of FA recognition from QoS perspective, uses improved K-SVD (Kernel Singular Value Decomposition) to learn dictionary by using the sparse representation of typical QoS characteristics of network multimedia traffics, and presents a network multimedia QoS class recognition method. Experiment results show that the proposed recognition method can achieve more accurate QoS class recognition than previous methods.
- Heterogeneous networks /
- Network multimedia traffic /
- Quality of Service (QoS) /
- Sparse representation /
- Traffic recognition /
- Dictionary learning

HTML全文

参考文献(24)

王再见, 董育宁, 张晖, 等. 一种异构网络多媒体业务QoS类弹性映射方法[J]. 电子与信息学报, 2013, 35(3): 709-714. doi: 10.3724/SP.J.1146.2012.00890.

WANG Zaijian, DONG Yuning, ZHANG Hui, et al. An elastic QoS class mapping method for multimedia traffic in heterogeneous wireless networks[J]. Journal of Electronics Information Technology, 2013, 35(3): 709-714. doi: 10.3724/ SP.J.1146.2012.00890.

王再见, 董育宁, 张晖, 等. 一种基于改进隐马尔可夫的多媒体业务分类算法[J]. 电子与信息学报, 2015, 37(2): 499-503. doi: 10.11999/JEIT140340.

WANG Zaijian, DONG Yuning, ZHANG Hui, et al. A multimedia traffic classification method based on improved hidden Markov model[J]. Journal of Electronics Information Technology, 2015, 37(2): 499-503. doi: 10.11999 /JEIT140340.

董育宁, 王再见, 房曙光, 等. 多媒体通信业务流识别与分类方法综述[J]. 南京邮电大学学报(自然科学版), 2013, 33(3): 35-44.

DONG Yuning, WANG Zaijian, FANG Shuguang, et al. Survey of methods for traffic identification and classification in multimedia communications[J]. Journal of Nanjing University of Posts and Telecommunications (Natural Science), 2013, 33(3): 35-44.

WANG Zaijian, DONG Yuning, and WANG Xinheng. A dynamic service class mapping scheme for different QoS domains using flow aggregation[J]. IEEE Systems Journal, 2015, 9(4): 1299-1310. doi: 10.1109/JSYST.2014.2351825.

WANG Zaijian, DONG Yuning, SHI Haixian, et al. Internet video traffic classification using QoS features[C]. 2016 the International Workshop on Computing, Networking and Communications (CNC), Hawaii, USA, 2016: 1-6. doi: 10.1109/ICCNC.2016.7440599.

MUHAMMAD M, GIAMBENE G, and COLA T. QoS Support in SGD-based high throughput satellite networks[J]. IEEE Transactions on Wireless Communications, 2016, 15(12): 847-849. doi: 10.1109/TWC.2016.2615618.

KAZEMI R, BOLOURSAZ M, SEYED M E, et al. Capacity bounds and detection schemes for data over voice[J]. IEEE Transactions on Vehicular Technology, 2016, 65(11): 8964-8977. doi: 10.1109/TVT.2016.2519926.

PRASETYA S, RAHMAT B, and SUSANTO E. Quality of service improvement with 802.11e EDCA scheme using enhanced adaptive contention window algorithm[C]. 2015 IEEE International Conference on Communication, Networks and Satellite (COMNESTAT), Bandung, Indonesia, 2015: 80-85. doi: 10.1109/COMNETSAT.2015.7434292.

FENG Kaiten, SU Wunci, and CHEN Chunyu. Comprehensive performance analysis and sleep window determination for IEEE 802.16 broadband wireless networks [J]. IEEE Transactions on Mobile Computing, 2016, 15(1): 74-91. doi: 10.1109/TMC.2015.2409881.

SUZNJEVIC M and MATIJASEVIC M. Trends in evolution of the network traffic of massively multiplayer online role-playing games[C]. 2015 13th International Conference on Telecommunications (ConTEL), Pirsa, Australia, 2015: 1-8. doi: 10.1109/ConTEL.2015.7231199.

MARKOVICH N and KRIEGER U. Statistical analysis and modeling of peer-to-peer multimedia traffic[J]. LNCS, 2011, 5233: 70-97. doi: https://doi.org/10.1007/978-3-642-02742- 0_4.

LIU Feng and LI Zhitang. A measurement and modeling study of P2P IPTV applications[C]. 2008 International Conference on Computational Intelligence and Security, CIS,08, Suzhou, China, 2008: 114-119. doi: 10.1109/CIS.2008. 112.

FARAHBAKHSH R, CRESPI N, CUEVAS, et al. Understanding the evolution of multimedia content in the internet through bittorrent glasses[J]. IEEE Network, 2013, 27(6): 80-88. doi: 10.1109/MNET.2013.6678931.

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. doi: 10.1109/TIFS.2012. 2223675.

MAHESHWARI S, MAHAPATRA S, KUMAR C, et al. A joint parametric prediction model for wireless internet traffic using Hidden Markov Model[J]. Wireless Networks, 2013, 19(6): 1171-1185. doi: 10.1007/s11276-012- 0525-1.

JING Ning, YANG Ming, CHENG Shaoyin, et al. An efficient SVM-based method for multi-class network traffic classification[C]. 2011 IEEE 30th International Performance Computing and Communications Conference (IPCCC), Orland, Florida, USA, 2011: 1-8. doi: 10.1109/PCCC.2011. 6108074.

FINSTERBUSCH M, RICHTER C, ROCHA E, et al. A survey of payload-based traffic classification approaches[J]. IEEE Communications Surveys Tutorials, 2014, 16(2): 1135-1156. doi: 10.1109/SURV.2013.100613.00161.

LI Qun, ZHANG Honggang, GUO Jun, et al. Reference-based scheme combined with K-SVD for scene image categorization [J]. IEEE Signal Processing Letters, 2013, 20(1): 67-70. doi: 10.1109/LSP.2012.2228852.

GOLD S. Hacking on the hoof[J]. Engineering and Technology, 2012, 7(3): 80-83. doi: 10.1049/et.2012.0313.

XU Yanbo, SIOHAN O, SIMCHA D, et al. Exemplar-based large vocabulary speech recognition using k-nearest neighbors[C]. 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Queensland, Australia, 2015: 5167-5171. doi: 10.1109/ICASSP.2015.7178956.

ZHANG Jun, CHEN Chao, XIANG Yang, et al. Classification of correlated Internet traffic flows[C]. 2012 IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), Liverpool, England, UK, 2012: 490-496. doi: 10.1109/TrustCom.2012. 105.

施引文献

资源附件(0)

访问统计