Cloud Demand Forecasting

Cloud Demand Forecasting is a predictive modeling approach that estimates future utilization of cloud computing resources by analyzing historical usage patterns, system metrics, and external variables to support capacity planning and resource allocation in cloud environments.

39

Publications

1.4K

Citations

127

Authors

66

Institutions

Publications per year

2017–2026

22

Authors

127

  1. CP
    Chang-Shing Perng

    IBM (United States), United States

    3 publications · 147 citations

    3

    h-index

  2. RC
    Rong Chang

    IBM (United States), United States

    3 publications · 147 citations

    3

    h-index

  3. YJ
    Yexi Jiang

    Florida International University, United States

    3 publications · 147 citations

    3

    h-index

  4. SS
    Saeed Sharifian

    Amirkabir University of Technology, Iran

    3 publications · 141 citations

    3

    h-index

  5. BL
    Baochun Li

    University of Toronto, Canada

    2 publications · 164 citations

    2

    h-index

1–5 of 127

Venues

  1. The Journal of Supercomputing

    2 publications · 119 citations

    2

    h-index

  2. Future Generation Computer Systems

    FGCS · 2 publications · 81 citations

    2

    h-index

  3. IEEE Transactions on Services Computing

    2 publications · 70 citations

    2

    h-index

  4. Business Informatics

    1 publications · 14 citations

    1

    h-index

  5. Proceedings of the IEEE

    1 publications · 58 citations

    1

    h-index

1–5