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Using Deep Neural Networks to Classify Hot-Cold Data Storage

SessionACM Student Research Competition Posters Display

DescriptionThe 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 accessed ("cold") data on cheaper technologies like Blu-ray disks or tape drives, and frequently accessed ("hot") data on faster but costlier mediums like Hard Disk Drives or Solid State Drives. Current data migration decisions rely on manual human judgment supported by simple algorithms not suitable for long-term predictions. To address this, our project aims to automate the process by training a deep neural network (DNN) on file metadata to predict data temperature upon upload. The model achieved promising initial results, with a 90.53% general accuracy in predicting data temperature. This automation could significantly improve the management and distribution of the vast research data generated at BNL.

Authors

Keene Lu

Northwestern University

Ai Kagawa

Brookhaven National Laboratory

Event Type

ACM Student Research Competition: Graduate Poster

ACM Student Research Competition: Undergraduate Poster

Posters