Modeling the YouTube stack: From packets to quality of experience
Modeling the YouTube stack: From packets to quality of experience
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DOI：
10.1016/j.comnet.2016.03.020
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发表时间：
2016-11
期刊：
影响因子：
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通讯作者：
Florian Wamser;P. Casas;Michael Seufert;Christian Moldovan;P. Tran-Gia;T. Hossfeld
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文献类型：
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作者：
Florian Wamser;P. Casas;Michael Seufert;Christian Moldovan;P. Tran-Gia;T. Hossfeld
YouTube是当今互联网上最受欢迎、流量最大的服务之一，并且永远改变了网络。因此，网络运营商被迫在其网络的设计、部署和优化中考虑它。驯服YouTube需要很好地理解完整的YouTube堆栈，从网络流媒体服务到应用程序本身。如今，了解YouTube各个功能之间的相互作用及其对流量和用户体验质量（QoE）的影响变得至关重要。在本文中，我们在不同的层，从生成的网络流量到观看YouTube视频的用户所感知的QoE，对YouTube堆栈进行了表征和建模。首先，我们提出了一个网络流量模型的YouTube流量控制机制，它允许了解YouTube如何规定视频流量给用户。其次，我们研究了流量在客户端是如何消耗的，为YouTube应用程序推导了一个简单的模型。第三，我们分析了最终用户的影响，并提出了一个模型的质量感知他们。该模型最终集成到一个系统中，用于基于真实的实时QoE的YouTube监控，对于运营商评估其网络性能以提供YouTube视频非常有用。所有模型的中心参数都是YouTube应用层的缓冲级别。本文为网络运营商提供了广泛的客观工具和模型，以更好地了解其网络中的YouTube流量，预测视频播放器的播放行为，并评估他们在向客户提供YouTube视频方面的实际表现。
YouTube is one of the most popular and volume-dominant services in today’s Internet, and has changed the web for ever. Consequently, network operators are forced to consider it in the design, deployment, and optimization of their networks. Taming YouTube requires a good understanding of the complete YouTube stack, from the network streaming service to the application itself. Understanding the interplays between individual YouTube functionalities and their implications for traffic and user Quality of Experience (QoE) becomes paramount nowadays. In this paper we characterize and model the YouTube stack at different layers, going from the generated network traffic to the QoE perceived by the users watching YouTube videos. Firstly, we present a network traffic model for the YouTube flow control mechanism, which permits to understand how YouTube provisions video traffic flows to users. Secondly, we investigate how traffic is consumed at the client side, deriving a simple model for the YouTube application. Thirdly, we analyze the implications for the end user, and present a model for the quality as perceived by them. This model is finally integrated into a system for real time QoE-based YouTube monitoring, highly useful to operators to assess the performance of their networks for provisioning YouTube videos. The central parameter for all the presented models is the buffer level at the YouTube application layer. This paper provides an extensive compendium of objective tools and models for network operators to better understand the YouTube traffic in their networks, to predict the playback behavior of the video player, and to assess how well they are doing in practice in delivering YouTube videos to their customers.