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October 9, 2025EPJ Web of ConferencesOpen Access

Exploring Data Caching Policy with Data Access Patterns from dCache System

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Authors

JAJ. M. AldrichUniversity of California, BerkeleyASAlex SimLawrence Berkeley National LaboratoryKWKesheng WuLawrence Berkeley National Laboratory

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Implication

Analysis demonstrates low predictive errors in dataset access patterns at Brookhaven National Laboratory, suggesting improved cache management strategies.

Key Points

  • The study shows significant reductions in predictive errors for dataset access patterns, enhancing cache efficiency.
  • Utilizing machine learning techniques, the model predicts access patterns and informs dataset pinning decisions effectively.
  • Integration of temporal trends and real-time access logs contributes to adaptive strategies for high-priority datasets.
  • Ongoing validation aims to optimize performance under realistic user workloads, crucial for large-scale data infrastructures.

Cite This Study

Aldrich et al. (2025) studied this question.

synapsesocial.com/papers/68e70db790569dd607ee6456https://doi.org/10.1051/epjconf/202533701340
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