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Multimedia Data Flow Traffic Classification Using Intelligent Models Based on Traffic Patterns

机译:基于流量模式的智能模型多媒体数据流流量分类

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Nowadays, there is high interest in modeling the type of multimedia traffic with the purpose of estimating the network resources required to guarantee the quality delivered to the user. In this work we propose a multimedia traffic classification model based on patterns that allows us to differentiate the type of traffic by using video streaming and network characteristics as input parameters. We show that there is low correlation between network parameters and the delivered video quality. Because of this, in addition to network parameters, we also add video streaming parameters in order to improve the efficiency of our system. Finally, it should be noted that, based on the objective video quality received by the user, we have extracted traffic patterns that we use to perfor
机译:如今,人们对建模多媒体流量的类型非常感兴趣,其目的在于估计为保证交付给用户的质量所需的网络资源。在这项工作中,我们提出了一种基于模式的多媒体流量分类模型,该模型允许我们使用视频流和网络特征作为输入参数来区分流量类型。我们表明,网络参数与交付的视频质量之间的相关性较低。因此,除了网络参数之外,我们还添加了视频流参数,以提高系统效率。最后,应该注意的是,基于用户接收到的客观视频质量,我们提取了用于执行的流量模式

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