CNS Core: Small: Online learning of cross-layer systems for robust and high-performance Internet video transmission

在过去的20年里，许多研究项目都使用机器学习来创建新的算法，这些算法从互联网上获取数据，并试图得出结论或做出决定。不幸的是，尽管这些算法中的许多在实验室环境中表现良好，但同样的学习算法在部署到真实的互联网上时有时表现不佳——或者它们最初表现良好，但在一段时间后衰减或产生无意义的结果。这些问题可能是由模型不匹配引起的，当实验室条件与现实世界不匹配时，或者是由数据集移动引起的，当环境与算法训练处理的条件不同时，或者是由于互联网的分散性，没有人或计算机可以看到整个网络的工作情况有多好或多差。无论原因是什么，这些问题仍然是在互联网上实际使用机器学习的令人烦恼的障碍。该研究项目将致力于发现和验证允许学习的网络算法随着时间的推移可靠地工作的方法，在变化的、不可预测的网络和用户群体的存在下，在实验室中忠实地模拟是具有挑战性的。该项目将探索用于网络视频传输的学习算法（约占互联网使用量的75%），如果学习在部署的同时和地点持续进行，而不是在实验室中进行，是否可以使其具有鲁棒性。该项目将围绕一个名为Puffer的公共研究实验展开：一个以研究为目的的直播视频网站。为了对代表互联网多样性和可变性的广泛网络路径进行采样，Puffer将同时向多达500名公众播放高清广播电视频道，这些频道将在Web浏览器中观看。Puffer将在持续的基础上对这些实时流量进行实验，自学视频流和拥塞控制的最佳算法和参数值，并将用户随机分配到不同的视频流算法，包括其他研究人员贡献的算法。如果成功，该项目将证明（并在一段持续的时间内评估）是否有可能使机器学习在现实生活中的网络环境中变得强大。该项目的网站是https://puffer.stanford.edu，它将用代码和数据存储库维护至少五年。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Over the last 20 years, many research projects have used machine learning to create new algorithms that take data over the Internet and try to draw conclusions or make decisions. Unfortunately, although many of these algorithms have performed well in a laboratory setting, the same learned algorithms sometimes underperform when deployed over the real Internet -- or they perform well initially, but decay or produce nonsensical results after some time has passed. These problems may be caused by model mismatch, when laboratory conditions do not match the real world, or by dataset shift, when the environment changes away from the conditions an algorithm was trained to handle, or by the decentralized nature of the Internet where no one person or computer can see how well or how poorly the entire network is working. Whatever the causes, these issues remain vexing obstacles to the practical use of machine learning on the Internet.This research project will work to discover and validate approaches that allow learned networking algorithms to work reliably over time, in the presence of a varying, unpredictable network and user population that are challenging to simulate faithfully in the lab. The project will explore whether learned algorithms for network video transmission, which accounts for about 75% of Internet usage, can be made robust if the learning happens continually, at the same time and place as deployment, instead of in the lab.The project will center around a public research experiment, called Puffer: a live video-streaming website run for research purposes. To sample a broad range of network paths that represent the diversity and variability of the Internet, Puffer will stream high-definition broadcast television channels to up to 500 members of the public simultaneously, to be viewed in a Web browser. Puffer will experiment on this live traffic on a continuous basis, teaching itself the best algorithms and parameter values for video streaming and congestion control, and randomizing users to different video-streaming algorithms, including ones contributed by other researchers. If successful, the project will demonstrate (and evaluate over a sustained period of time) whether it is possible to make machine learning robust in this real-life networked setting.The project website is https://puffer.stanford.edu, which will be maintained with code and data repositories for at least five years.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.