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首页> 外文期刊>IEEE transactions on network and service management >ViCrypt to the Rescue: Real-Time, Machine-Learning-Driven Video-QoE Monitoring for Encrypted Streaming Traffic
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ViCrypt to the Rescue: Real-Time, Machine-Learning-Driven Video-QoE Monitoring for Encrypted Streaming Traffic

机译：Vicryptpt to Vicrypt：实时，机器学习驱动的视频QoE监控，用于加密流流量

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摘要

Video streaming is the killer application of the Internet today. In this article, we address the problem of real-time, passive Quality-of-Experience (QoE) monitoring of HTTP Adaptive Video Streaming (HAS), from the Internet-Service-Provider (ISP) perspective - i.e., relying exclusively on in-network traffic measurements. Given the wide adoption of end-to-end encryption, we resort to machine-learning (ML) models to estimate multiple key video-QoE indicators (KQIs) from the analysis of the encrypted traffic. We present ViCrypt, an ML-driven monitoring solution able to infer the most important KQIs for HTTP Adaptive Streaming (HAS), namely stalling, initial delay, video resolution, and average video bitrate. ViCrypt performs estimations in real-time, during the playback of an ongoing video-streaming session, with a fine-grained temporal resolution of just one second. For this, it relies on lightweight, stream-like features continuously extracted from the encrypted stream of packets. Empirical evaluations on a large and heterogeneous corpus of YouTube measurements show that ViCrypt can infer the targeted KQIs with high accuracy, enabling large-scale passive video-QoE monitoring and proactive QoE-aware traffic management. Different from the state of the art, and besides real-time operation, ViCrypt is not bound to coarse-grained KQI-classes, providing better and sharper insights than other solutions. Finally, ViCrypt does not require chunk-detection approaches for feature extraction, significantly reducing the complexity of the monitoring approach, and potentially improving on generalization to different HAS protocols used by other video-streaming services such as Netflix and Amazon.
机译：视频流是今天互联网的杀手应用程序。在本文中，我们解决了来自Internet-Service-Provider（ISP）透视图的实时，被动质量（QoE）监视HTTP自适应视频流（具有）的问题 - 即，仅依赖于依赖于-Network流量测量。鉴于集端到端加密广泛采用，我们求助于机器学习（ML）模型来估算来自加密流量的分析的多个关键视频QoE指示符（KQIS）。我们呈现VICrypt，一个能够推断用于HTTP自适应流（具有）的最重要的KQI的ML驱动的监控解决方案，即停止，初始延迟，视频分辨率和平均视频比特率。 vicrypt在播放持续的视频流播放期间实时执行估计，其具有一秒钟的细粒度时间分辨率。为此，它依赖于轻量级，类似的流式特征从加密的数据包流中连续提取。对YouTube测量的大型和异构语料库的实证评估表明，Vicrypt可以高精度地推断目标KQI，从而实现大规模的被动视频QoE监控和主动QoE感知流量管理。与本领域的状态不同，除了实时操作之外，Vicrypt不受粗粒程级级，提供比其他解决方案更好，更清晰的见解。最后，Vicrypt不需要特征提取的块检测方法，显着降低了监视方法的复杂性，并且可能改善不同的概括与其他视频流服务（如Netflix和Amazon）使用的协议。

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