This study explores how fallout from the changing public health policy around COVID-19 has changed how researchers access and process their science experiments. Using a combination of techniques from statistical analysis and machine learning, we conduct a retrospective analysis of historical network data for a period around the stay-at-home orders that took place in March 2020. Our analysis takes data from the entire ESnet infrastructure to explore DOE high-performance computing (HPC) resources at OLCF, ALCF, and NERSC, as well as User sites such as PNNL and JLAB. We look at detecting and quantifying changes in site activity using a combination of t-Distributed Stochastic Neighbor Embedding (t-SNE) and decision tree analysis. Our findings bring insights into the working patterns and impact on data volume movements, particularly during late-night hours and weekends.

中文翻译：

---

基于机器学习的 COVID-19 大流行对美国研究网络影响的分析

这项研究探讨了围绕 COVID-19 不断变化的公共卫生政策的影响如何改变了研究人员访问和处理他们的科学实验的方式。结合统计分析和机器学习的技术，我们对 2020 年 3 月发生的居家订单期间的历史网络数据进行了回顾性分析。我们的分析从整个 ESnet 基础设施中获取数据以进行探索OLCF、ALCF 和 NERSC 以及 PNNL 和 JLAB 等用户站点的 DOE 高性能计算 (HPC) 资源。我们着眼于使用 t 分布随机邻域嵌入 (t-SNE) 和决策树分析的组合来检测和量化站点活动的变化。我们的发现带来了对工作模式和对数据量移动的影响的洞察，