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March 26, 2026ACM Computing SurveysOpen Access

Machine Learning-Based Caching and Tiering in Modern Data Storage Systems: A Survey

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Authors

GSGeorge SavvaCyprus University of TechnologyEKElena KakoulliCyprus University of TechnologyHHHerodotos HerodotouCyprus University of Technology

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Overview

A survey reveals machine learning optimizes caching and tiering in data storage systems, suggesting innovative pathways for performance enhancement.

Key Points

  • The research aims to review and assess machine learning-based caching and tiering strategies in modern data storage systems.
  • Conducted a comprehensive survey of existing literature on ML caching and tiering policies.
  • Examined theoretical foundations and practical implementations of these ML techniques.
  • Analysed features, baselines for comparison, and evaluation metrics
  • Identified emerging trends and future directions in data storage systems.
  • ML-based policies increase cache hit rates and reduce latency.
  • These techniques provide cost-effective solutions for data placement across storage media.
  • The study outlines most common features of caching and tiering policies.

Cite This Study

Savva et al. (2026) studied this question.

synapsesocial.com/papers/69c4cd80fdc3bde448919ddfhttps://doi.org/10.1145/3803857
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