Network Multimedia QoS Class Recognition Based on Improved K-SVD

WANG Zaijian; DONG Yuning; TANG Pingping; YANG Lingyun; ZHANG Hui

doi:10.11999/JEIT170133

Volume 39 Issue 12

Dec. 2017

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Article Navigation > Journal of Electronics & Information Technology > 2017 > 39(12): 3023-3029

WANG Zaijian, DONG Yuning, TANG Pingping, YANG Lingyun, ZHANG Hui. Network Multimedia QoS Class Recognition Based on Improved K-SVD[J]. Journal of Electronics & Information Technology, 2017, 39(12): 3023-3029. doi: 10.11999/JEIT170133

Citation:

WANG Zaijian, DONG Yuning, TANG Pingping, YANG Lingyun, ZHANG Hui. Network Multimedia QoS Class Recognition Based on Improved K-SVD[J]. Journal of Electronics & Information Technology, 2017, 39(12): 3023-3029. doi: 10.11999/JEIT170133

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Network Multimedia QoS Class Recognition Based on Improved K-SVD

doi: 10.11999/JEIT170133

(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)

Received Date: 2017-02-17
Rev Recd Date: 2017-10-01
Publish Date: 2017-12-19

Abstract

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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References(24)

References

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