Machine learning-based analysis of COVID-19 pandemic impact on US research networks

IF 2.2 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
M. Kiran, Scott Campbell, F. Wala, Nick Buraglio, I. Monga
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引用次数: 0

Abstract

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.
基于机器学习的COVID-19大流行对美国研究网络影响分析
本研究探讨了围绕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获取和处理科学实验的方式。我们结合了统计分析和机器学习技术,对2020年3月发生的“居家令”前后一段时间的历史网络数据进行了回顾性分析。我们的分析采用了来自整个ESnet基础设施的数据,以探索OLCF、ALCF和NERSC以及PNNL和JLAB等用户站点的能源部高性能计算(HPC)资源。我们着眼于使用t分布随机邻居嵌入(t-SNE)和决策树分析相结合来检测和量化站点活动的变化。我们的研究结果深入了解了工作模式及其对数据量移动的影响,尤其是在深夜和周末。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
ACM Sigcomm Computer Communication Review
ACM Sigcomm Computer Communication Review 工程技术-计算机:信息系统
CiteScore
6.90
自引率
3.60%
发文量
20
审稿时长
4-8 weeks
期刊介绍: Computer Communication Review (CCR) is an online publication of the ACM Special Interest Group on Data Communication (SIGCOMM) and publishes articles on topics within the SIG''s field of interest. Technical papers accepted to CCR typically report on practical advances or the practical applications of theoretical advances. CCR serves as a forum for interesting and novel ideas at an early stage in their development. The focus is on timely dissemination of new ideas that may help trigger additional investigations. While the innovation and timeliness are the major criteria for its acceptance, technical robustness and readability will also be considered in the review process. We particularly encourage papers with early evaluation or feasibility studies.
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