Issue |
EPJ Web of Conf.
Volume 295, 2024
26th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2023)
|
|
---|---|---|
Article Number | 01044 | |
Number of page(s) | 9 | |
Section | Data and Metadata Organization, Management and Access | |
DOI | https://doi.org/10.1051/epjconf/202429501044 | |
Published online | 06 May 2024 |
https://doi.org/10.1051/epjconf/202429501044
Predicting Resource Utilization Trends with Southern California Petabyte Scale Cache
1 University of California at Berkeley, Berkeley, CA, USA
2 Lawrence Berkeley National Laboratory, Berkeley, CA, USA
3 Energy Sciences Network, Berkeley, CA, USA
4 University of California at San Diego, La Jolla, CA, USA
5 California Institute of Technology, Pasadena, CA, USA
* e-mail: caitlinsim@berkeley.edu
** e-mail: kwu@lbl.gov
*** e-mail: asim@lbl.gov
**** e-mail: imonga@es.net
† e-mail: chin@es.net
‡ e-mail: dhazen@es.net
§ e-mail: fkw@ucsd.edu
¶ e-mail: didavila@ucsd.edu
∥ e-mail: newman@hep.caltech.edu
** e-mail: jbalcas@caltech.edu
Published online: 6 May 2024
Large community of high-energy physicists share their data all around world making it necessary to ship a large number of files over wide- area networks. Regional disk caches such as the Southern California Petabyte Scale Cache have been deployed to reduce the data access latency. We observe that about 94% of the requested data volume were served from this cache, without remote transfers, between Sep. 2022 and July 2023. In this paper, we show the predictability of the resource utilization by exploring the trends of recent cache usage. The time series based prediction is made with a machine learning approach and the prediction errors are small relative to the variation in the input data. This work would help understanding the characteristics of the resource utilization and plan for additional deployments of caches in the future.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.