Learning in situ: a randomized experiment in video streaming
Learning in situ: a randomized experiment in video streaming
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发表时间：
2019-06
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通讯作者：
Francis Y. Yan;Hudson Ayers;Chenzhi Zhu;Sadjad Fouladi;James Hong;Keyi Zhang;P. Levis;Keith Winstein
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作者：
Francis Y. Yan;Hudson Ayers;Chenzhi Zhu;Sadjad Fouladi;James Hong;Keyi Zhang;P. Levis;Keith Winstein
我们描述了一个随机对照试验的视频流算法的比特率选择和网络预测的结果。在过去的八个月里，我们通过互联网向56，000名用户播放了14.2年的视频。会话在算法之间以盲态方式随机化，并记录客户遥测以供分析。我们发现，在这个现实世界的设置，它是很难复杂的或机器学习的控制方案优于“简单”的计划（基于缓冲区的控制），尽管在网络仿真器或模拟器的良好性能。我们进行了统计分析，发现网络和算法行为的可变性和重尾性质为这一领域的鲁棒学习算法创造了障碍。我们开发了一种ABR算法，鲁棒性优于其他计划在实践中，通过将经典控制与学习的网络预测器相结合，在现场对来自真实的部署环境的数据进行监督学习训练。为了支持进一步的调查，我们每天都会发布一份痕迹和结果的档案，并将向社区开放我们正在进行的研究。我们欢迎其他研究人员使用这个平台来开发和验证比特率选择，网络预测和拥塞控制的新算法。
We describe the results of a randomized controlled trial of video-streaming algorithms for bitrate selection and network prediction. Over the last eight months, we have streamed 14.2 years of video to 56,000 users across the Internet. Sessions are randomized in blinded fashion among algorithms, and client telemetry is recorded for analysis. We found that in this real-world setting, it is difficult for sophisticated or machine-learned control schemes to outperform a "simple" scheme (buffer-based control), notwithstanding good performance in network emulators or simulators. We performed a statistical analysis and found that the variability and heavy-tailed nature of network and algorithm behavior create hurdles for robust learned algorithms in this area. We developed an ABR algorithm that robustly outperforms other schemes in practice, by combining classical control with a learned network predictor, trained with supervised learning in situ on data from the real deployment environment. To support further investigation, we are publishing an archive of traces and results each day, and will open our ongoing study to the community. We welcome other researchers to use this platform to develop and validate new algorithms for bitrate selection, network prediction, and congestion control.