Data Access Pattern Analysis fordCache Storage System
Amanda Pereira1 , Alex Sim2 , John Wu2 , Shinjae Yoo3 , Hiro Ito3
Drake University1 , Lawrence Berkeley National Laboratory2 , Brookhaven National Laboratory3

I. I NTRODUCTION
High-Energy physicists routinely analyze files containing
particle collision data for their research. The US CMS collaboration [1] [2] has set up a high performance storage system(HPSS) for their data files and uses the dCache system [3]as a disk cache in front of the HPSS. Users retrieve this data
through a request to the dCache system. Upon request, dCachesearches the local cache for the file. Files stored locally can beaccessed without involving the HPSS. Files that are not on diskneed to be retrieved from the HPSS prior to access. Predicting
when a file is needed and moving it to disk ahead of time canreduce access time. To enable such predictions, we plan to
study the data access patterns of the dCache system. Numerous
efforts have been reported on such data access patterns [4],however, these earlier studies primarily focus on individualfiles. In this work, we plan to focus on file groups that could
be more easily managed by the system administrators.
II. A NALYSIS
Data transfer logs and metadata information were used
to determine patterns in the popularity of datasets during
previous time periods. Files must first be grouped into datasets
according to information found in the file path. Access patterns
can be studied at the dataset level for more efficient analysis.
Initial groupings are determined by the energy measurementof particles being studied using a unit called teraelectronvolts(TeV). Most files in the metadata information provide accesspatterns statistics for files containing information about particles with 13TeV of energy. Many of these files contain ”mc15”
in front of the ”13TeV” in their file path. The research focus
is narrowed to files with “mc15” and ”13TeV” in their path.
Over 3/4 of the files in this group have a path that contains”EVNT”. Popularity calculation and analysis was done for”EVNT” datasets as a result of the higher access counts.
Dataset popularity is calculated by dividing the number oftimes a dataset was accessed in a given time period by thetotal number of files in the dataset. The popularity for Mc15
13TeV group datasets containing ”EVNT” on April 3-9, 2021
is displayed in figure 1. The peaks in figure 1 represent themore popular ”EVNT” datasets during the week.
III. C ONCLUSION
Certain events have a popularity almost 5x that of otherevents in figure 1. These results suggest that for maximum
storage system efficiency, popular event datasets should be
stored locally during the time period in which they are popular,

Fig. 1.

Mc15 13TeV event dataset popularity April 3, 2021

and moved to the HPSS for storage when they are not popular.
The development of tools for past data access pattern analysis
is useful for forecasting dataset access patterns in future time
periods. Predicting future dataset popularity is necessary forcaching the appropriate data and improving the application
performance of the dCache system.
ACKNOWLEDGMENT
This work was supported in part by the Office of Advanced Scientific Computing Research, Office of Science,of the U.S. Department of Energy under Contract No. DEAC02-05CH11231, and also used resources of the National
Energy Research Scientific Computing Center (NERSC). The
author would like to thank my mentors, Alex Sim and JohnWu, at Scientific Data Management (SDM) Group, Lawrence
Berkeley National Laboratory. This work was also supported
in part by the U.S. Department of Energy, Office of Science,
Office of Workforce Development for Teachers and Scientists(WDTS) under the Science Undergraduate Laboratory Internship (SULI) program.
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