Machine Learning For Tiering

Machine Learning for Tiering is a computational framework that employs machine learning models to dynamically predict and optimize data placement across storage tiers based on access patterns, thereby distinguishing itself from rule-based or heuristic tiering strategies through its adaptive learning capability.

5

Publications

102

Citations

18

Authors

8

Institutions

Publications per year

2017–2026

4

Authors

18

  1. BA
    Bartu Arslan

    Yaşar University, Türkiye

    2 publications · 56 citations

    2

    h-index

  2. BY
    Banu Y. Ekren

    Yaşar University, Türkiye

    2 publications · 56 citations

    2

    h-index

  3. GS
    Galen Swint

    Georgia Institute of Technology, United States

    1 publications · 14 citations

    1

    h-index

  4. AS
    Akhil Sahai

    Hewlett-Packard (United States), United States

    1 publications · 14 citations

    1

    h-index

  5. BM
    Barbara Martelli

    Istituto Nazionale di Fisica Nucleare, Centro Nazionale Analisi Fotogrammi, Italy

    1 publications · 14 citations

    1

    h-index

1–5 of 18

Venues

  1. Symmetry

    1 publications · 18 citations

    1

    h-index

  2. International Journal of Production Research

    1 publications · 20 citations

    1

    h-index

  3. 1

    h-index

  4. International Workshop on Quality of Service

    IWQoS · 1 publications · 14 citations

    1

    h-index

1–4