论文标题
部分可观测时空混沌系统的无模型预测
Comparison of Popular Video Conferencing Apps Using Client-side Measurements on Different Backhaul Networks
论文作者
论文摘要
储层计算是预测湍流的有力工具，其简单的架构具有处理大型系统的计算效率。然而，其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间，然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用，该系统模拟了湍流的若干特征。我们表明，使用径向基函数作为非线性投影器，即使只有部分观测并且不知道控制方程，也能稳健地捕捉复杂的系统非线性。最后，我们表明，当测量稀疏、不完整且带有噪声，甚至控制方程变得不准确时，我们的网络仍然可以产生相当准确的预测，从而为实际湍流系统的无模型预测铺平了道路。
Video conferencing platforms have been appropriated during the COVID-19 pandemic for different purposes, including classroom teaching. However, the platforms are not designed for many of these objectives. When users, like educationists, select a platform, it is unclear which platform will perform better given the same network and hardware resources to meet the required Quality of Experience (QoE). Similarly, when developers design a new video conferencing platform, they do not have clear guidelines for making design choices given the QoE requirements. In this paper, we provide a set of networks and systems measurements, and quantitative user studies to measure the performance of video conferencing apps in terms of both, Quality of Service (QoS) and QoE. Using those metrics, we measure the performance of Google Meet, Microsoft Teams, and Zoom, which are three popular platforms in education and business. We find a substantial difference in how the three apps treat video and audio streams. We see that their choice of treatment affects their consumption of hardware resources. Our quantitative user studies confirm the findings of our quantitative measurements. While each platform has its benefits, we find that no app is ideal. A user can choose a suitable platform depending on which of the following, audio, video, or network bandwidth, CPU, or memory are more important.
