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UID:submissions.supercomputing.org_SC23_sess299_spostu113@linklings.com
SUMMARY:Using Deep Neural Networks to Classify Hot-Cold Data Storage
DESCRIPTION:Keene Lu (Northwestern University) and Ai Kagawa (Brookhaven N
 ational Laboratory)\n\nThe Scientific Data and Computing Center (SDCC) at 
 Brookhaven National Laboratory manages a data storage system with millions
  of files totaling petabytes of data. To optimize costs, they use a multi-
 tiered storage approach based on data temperature, storing infrequently ac
 cessed ("cold") data on cheaper technologies like Blu-ray disks or tape dr
 ives, and frequently accessed ("hot") data on faster but costlier mediums 
 like Hard Disk Drives or Solid State Drives. Current data migration decisi
 ons rely on manual human judgment supported by simple algorithms not suita
 ble for long-term predictions. To address this, our project aims to automa
 te the process by training a deep neural network (DNN) on file metadata to
  predict data temperature upon upload. The model achieved promising initia
 l results, with a 90.53% general accuracy in predicting data temperature. 
 This automation could significantly improve the management and distributio
 n of the vast research data generated at BNL.\n\nTag: Artificial Intellige
 nce/Machine Learning, Algorithms, Applications, Architecture and Networks,
  Cloud Computing, Distributed Computing, Data Analysis, Visualization, and
  Storage, Performance Measurement, Modeling, and Tools, Programming Framew
 orks and System Software\n\nRegistration Category: Tech Program Reg Pass, 
 Exhibits Reg Pass\n\n
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