Veritas：从视频流跟踪中提取因果关系
Veritas: Answering Causal Queries from Video Streaming Traces
摘要：
在本文中，我们试图回答假设问题-即，给定已部署的网络系统的记录数据，如果我们改变系统的设计，性能会受到什么影响（这一任务也称为因果推理）。我们做了三个贡献。首先，我们在自适应比特率视频流的背景下揭示了因果推理的复杂性，这是一个具有挑战性的领域，其中会话期间的网络条件充当一系列潜在和混杂变量，并且会话中任何点的变化都会对会话的其余部分产生级联影响。其次，我们提出了Veritas，一个新的框架，解决了视频流的因果推理，而不诉诸随机试验。Veritas的核心是一个易于解释的特定于域的ML模型（嵌入式隐马尔可夫模型），它将潜在的随机过程（视频会话可以实现的固有带宽）与实际观察（下载时间）联系起来，同时利用TCP状态（例如，拥塞窗口）。我们通过仿真测试平台上的实验表明，Veritas可以回答两个反事实查询（例如，完成的视频会话的性能使其使用不同的缓冲器大小）和介入查询（例如，估计正在进行的会话中的下一个块的每个可能的视频质量选择的下载时间）。通过这样做，Veritas实现了接近理想预言的准确性，同时显著优于常用的基线方法和Fugu（一种现成的神经网络），这两种方法都没有考虑因果效应。
英文摘要：
In this paper, we seek to answer what-if questions - i.e., given recorded data of an existing deployed networked system, what would be the performance impact if we changed the design of the system (a task also known as causal inference). We make three contributions. First, we expose the complexity of causal inference in the context of adaptive bit rate video streaming, a challenging domain where the network conditions during the session act as a sequence of latent and confounding variables, and a change at any point in the session has a cascading impact on the rest of the session. Second, we present Veritas, a novel framework that tackles causal reasoning for video streaming without resorting to randomised trials. Integral to Veritas is an easy to interpret domain-specific ML model (an embedded Hidden Markov Model) that relates the latent stochastic process (intrinsic bandwidth that the video session can achieve) to actual observations (download times) while exploiting control variables such as the TCP state (e.g., congestion window) observed at the start of the download of video chunks. We show through experiments on an emulation testbed that Veritas can answer both counterfactual queries (e.g., the performance of a completed video session had it used a different buffer size) and interventional queries (e.g., estimating the download time for every possible video quality choice for the next chunk in a session in progress). In doing so, Veritas achieves accuracy close to an ideal oracle, while significantly outperforming both a commonly used baseline approach, and Fugu (an off-the-shelf neural network) neither of which account for causal effects.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
