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.
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.