使用改進(jìn)K-SVD的網(wǎng)絡(luò)多媒體業(yè)務(wù)QoS類識(shí)別

王再見; 董育寧; 湯萍萍; 楊凌云; 張暉

doi:10.11999/JEIT170133

使用改進(jìn)K-SVD的網(wǎng)絡(luò)多媒體業(yè)務(wù)QoS類識(shí)別

doi: 10.11999/JEIT170133

基金項(xiàng)目:

國家自然科學(xué)基金(61401004, 61271233, 61471203), 2016年安徽省高校領(lǐng)軍人才引進(jìn)與培育計(jì)劃項(xiàng)目(gxfxZD2016013)，安徽師范大學(xué)博士科研啟動(dòng)基金(2016XJJ129)

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出版歷程
- 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: 該文基于網(wǎng)絡(luò)多媒體業(yè)務(wù)QoS(Quality of Service)特征特點(diǎn)，提出網(wǎng)絡(luò)業(yè)務(wù)QoS類識(shí)別算法。探索了新的多媒體業(yè)務(wù)QoS類劃分模式，在QoS分類的基礎(chǔ)上，可以通過將具有相同或相似QoS需求特征的業(yè)務(wù)流聚集生成聚集流。聚集流劃分使用較少的QoS特征，借助聚集流可以在合理的粒度上區(qū)分多媒體業(yè)務(wù)。該文從QoS特征出發(fā)分析了聚集流識(shí)別的特點(diǎn)，利用網(wǎng)絡(luò)多媒體業(yè)務(wù)典型QoS特征的稀疏性，使用改進(jìn)K-SVD(Kernel Singular Value Decomposition)進(jìn)行字典學(xué)習(xí)，實(shí)現(xiàn)網(wǎng)絡(luò)多媒體業(yè)務(wù)QoS類識(shí)別。實(shí)驗(yàn)結(jié)果表明，該文算法比現(xiàn)有方法具有更高的QoS類識(shí)別準(zhǔn)確性。
- 異構(gòu)網(wǎng)絡(luò) /
- 網(wǎng)絡(luò)多媒體 /
- QoS /
- 稀疏表示 /
- 業(yè)務(wù)流識(shí)別 /
- 字典訓(xùn)練
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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