DeepQoE:利用深度学习从加密流量中实时测量视频QoE

Meng Shen, Jinpeng Zhang, Ke Xu, Liehuang Zhu, Jiangchuan Liu, Xiaojiang Du
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引用次数: 17

摘要

随着互联网视频流量的急剧增加,视频体验质量(QoE)测量变得更加重要,它为网络运营商提供了洞察其视频交付服务质量的方法。然而,端到端加密协议(如SSL/TLS)的广泛采用为QoE监控设置了障碍,因为明文流量中最有价值的指标在加密后不再可用。现有的加密流量视频QoE度量研究仅支持粗粒度的QoE度量或精度较低。在本文中,我们提出了DeepQoE,这是一种能够从加密流量中实时测量视频QoE的新方法。我们总结了关键的细粒度QoE指标,包括启动延迟、再缓冲和视频分辨率。为了实现这些指标的准确和实时推断,我们通过使用具有复杂输入和架构设计的卷积神经网络(cnn)来构建DeepQoE。更具体地说,DeepQoE仅利用上游流量中的数据包往返时间(RTT)作为其输入。从两个流行的内容提供商(即YouTube和Bilibili)收集的真实数据集的评估结果表明,与最先进的方法相比,DeepQoE可以将QoE测量精度提高22%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
DeepQoE: Real-time Measurement of Video QoE from Encrypted Traffic with Deep Learning
With the dramatic increase of video traffic on the Internet, video quality of experience (QoE) measurement becomes even more important, which provides network operators with an insight into the quality of their video delivery services. The widespread adoption of end-to-end encryption protocols such as SSL/TLS, however, sets a barrier to QoE monitoring as the most valuable indicators in cleartext traffic are no longer available after encryption. Existing studies on video QoE measurement in encrypted traffic support only coarse-grained QoE metrics or suffer from low accuracy. In this paper, we propose DeepQoE, a new approach that enables real-time video QoE measurement from encrypted traffic. We summarize critical fine-grained QoE metrics, including startup delay, rebuffering, and video resolutions. In order to achieve accurate and real-time inference of these metrics, we build DeepQoE by employing Convolutional Neural Networks (CNNs) with a sophisticated input and architecture design. More specifically, DeepQoE only leverages packet Round-Trip Time (RTT) in upstream traffic as its input. Evaluation results with real-world datasets collected from two popular content providers (i.e., YouTube and Bilibili) show that DeepQoE can improve QoE measurement accuracy by up to 22% over the state-of-the-art methods.
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