EPJ Web of Conferences 295, 01044 (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

Abstract

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.