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)

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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

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参考文献(24)

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