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DTSTAMP:20250626T234539Z
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UID:submissions.supercomputing.org_SC24_sess487_post259@linklings.com
SUMMARY:Predicting Dataset Popularity for Improved Distributed Content Cac
 hing in High Energy Physics
DESCRIPTION:Malavikha Sudarshan (University of California, Berkeley)\n\nIn
  High Energy Physics (HEP), large-scale experiments generate massive amoun
 ts of data that are distributed globally. To reduce redundant data transfe
 rs and improve analysis efficiency, a disk caching system named XCache is 
 used to manage data accesses. By analyzing 11 months of access logs (4.5 m
 illion requests), we identified patterns in dataset usage and developed a 
 predictive model to forecast the popularity of frequently accessed dataset
 s. \n\nBased on extensive exploratory data analysis, we found that pinging
  the most popular datasets (pinning these in the cache) could significantl
 y improve access efficiency, and we implemented an LSTM model to predict d
 ataset accesses and optimize cache policies. \n\nThe model demonstrates st
 rong predictive performance with a low mean relative error of 0.779 across
  training and test datasets. Future work will incorporate anomaly detectio
 n techniques to improve robustness. This study highlights the potential of
  LSTM models in optimizing distributed content caching in HEP.\n\nRegistra
 tion Category: Tech Program Reg Pass, Exhibits Reg Pass\n\nSession Chairs:
  Ayesha Afzal (Friedrich-Alexander University, Erlangen-Nuremberg; Erlange
 n National High Performance Computing Center); Sally Ellingson (University
  of Kentucky); and Alan Sussman (University of Maryland)\n\n
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