To address the issue of finegrained classification of Internet multimedia traffic from a Quality of Service(QoS) perspective with a suitable granularity, this paper defines a new set of QoS classes and presents a modi...To address the issue of finegrained classification of Internet multimedia traffic from a Quality of Service(QoS) perspective with a suitable granularity, this paper defines a new set of QoS classes and presents a modified K-Singular Value Decomposition(K-SVD) method for multimedia identification. After analyzing several instances of typical Internet multimedia traffic captured in a campus network, this paper defines a new set of QoS classes according to the difference in downstream/upstream rates and proposes a modified K-SVD method that can automatically search for underlying structural patterns in the QoS characteristic space. We define bagQoS-words as the set of specific QoS local patterns, which can be expressed by core QoS characteristics. After the dictionary is constructed with an excess quantity of bag-QoSwords, Locality Constrained Feature Coding(LCFC) features of QoS classes are extracted. By associating a set of characteristics with a percentage of error, an objective function is formulated. In accordance with the modified K-SVD, Internet multimedia traffic can be classified into a corresponding QoS class with a linear Support Vector Machines(SVM) clas-sifier. Our experimental results demonstrate the feasibility of the proposed classification method.展开更多
准确、高效的业务流识别与分类是保障多媒体通信端到端QoS(Quality of Service)、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,且在包/流水平统计特征多样性,业务统计特征有效选取直接关系到识别和分类方...准确、高效的业务流识别与分类是保障多媒体通信端到端QoS(Quality of Service)、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,且在包/流水平统计特征多样性,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。在介绍相关研究成果的基础上,文中从业务特征角度对现有技术进行分类,进而对比各类方法的性能,同时在探讨当前业务流识别方法存在对新业务识别准确度不高、实时性不足等问题的基础上,结合跨域QoS类映射弹性需求的特点,给出跨域QoS类映射中多媒体业务识别架构。整个架构的目标是准确、高效地识别多媒体流,为聚集流的形成做好前期准备,为保障高效的端到端QoS提供技术支撑。最后,总结了发展趋势和面临的挑战。展开更多
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...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。展开更多
基金supported in part by the National Natural Science Foundation of China (NO. 61401004, 61271233, 60972038)Plan of introduction and cultivation of university leading talents in Anhui (No.gxfxZ D2016013)+3 种基金the Natural Science Foundation of the Higher Education Institutions of Anhui Province, China (No. KJ2010B357)Startup Project of Anhui Normal University Doctor Scientific Research (No.2016XJJ129)the US National Science Foundation under grants CNS1702957 and ACI-1642133the Wireless Engineering Research and Education Center at Auburn University
文摘To address the issue of finegrained classification of Internet multimedia traffic from a Quality of Service(QoS) perspective with a suitable granularity, this paper defines a new set of QoS classes and presents a modified K-Singular Value Decomposition(K-SVD) method for multimedia identification. After analyzing several instances of typical Internet multimedia traffic captured in a campus network, this paper defines a new set of QoS classes according to the difference in downstream/upstream rates and proposes a modified K-SVD method that can automatically search for underlying structural patterns in the QoS characteristic space. We define bagQoS-words as the set of specific QoS local patterns, which can be expressed by core QoS characteristics. After the dictionary is constructed with an excess quantity of bag-QoSwords, Locality Constrained Feature Coding(LCFC) features of QoS classes are extracted. By associating a set of characteristics with a percentage of error, an objective function is formulated. In accordance with the modified K-SVD, Internet multimedia traffic can be classified into a corresponding QoS class with a linear Support Vector Machines(SVM) clas-sifier. Our experimental results demonstrate the feasibility of the proposed classification method.
文摘准确、高效的业务流识别与分类是保障多媒体通信端到端QoS(Quality of Service)、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,且在包/流水平统计特征多样性,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。在介绍相关研究成果的基础上,文中从业务特征角度对现有技术进行分类,进而对比各类方法的性能,同时在探讨当前业务流识别方法存在对新业务识别准确度不高、实时性不足等问题的基础上,结合跨域QoS类映射弹性需求的特点,给出跨域QoS类映射中多媒体业务识别架构。整个架构的目标是准确、高效地识别多媒体流,为聚集流的形成做好前期准备,为保障高效的端到端QoS提供技术支撑。最后,总结了发展趋势和面临的挑战。
文摘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。
