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UID:submissions.supercomputing.org_SC25_sess620_post195@linklings.com
SUMMARY:Analyzing Dataset Popularity for Optimizing In-Network Storage
DESCRIPTION:Gunwoo Kim (University of California, Davis) and Alex Sim and 
 Kesheng Wu (ESnet; Lawrence Berkeley National Laboratory (LBNL))\n\nIn hig
 h energy physics (HEP), large-scale experiments produce enormous data volu
 mes that are distributed across global storage systems. To reduce redundan
 t transfers and improve efficiency, disk caching systems such as XCache ar
 e deployed, but their effectiveness depends on good caching policy. Our re
 search asks: can we find patterns and reliably predict dataset popularity?
  This work investigates dataset-level “pinning,” where sets of files are r
 etained in cache to improve hit rates. We explore the use of Hawkes proces
 ses, a statistical model, to model bursty, event-driven dataset popularity
 , a novel approach compared to previous efforts. Preliminary results sugge
 st this framework improves predictability of future access patterns, there
 by guiding more effective caching strategies. The poster will present our 
 methodology, experimental setup, and early evaluation results, highlightin
 g both the promise and current limitations of this approach.\n\nTag: Resea
 rch & ACM SRC Posters\n\nRegistration Category: Technical Program Reg Pass
 \n\nSession Chairs: Kento Sato (RIKEN Center for Computational Science (R-
 CCS)); Anja Gerbes (Georg-August-Universität Göttingen); and Chris Schlipa
 lius (Pawsey Supercomputing Research Centre; Commonwealth Scientific and I
 ndustrial Research Organisation (CSIRO), Australia)\n\n
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