Resource Demand Modeling

Resource Demand Modeling is a predictive analytical framework that characterizes and quantifies the expected consumption patterns of computing resources in cloud environments by integrating historical usage data, workload profiles, and scalability requirements to inform capacity provisioning decisions.

455

Publications

19.6K

Citations

1.4K

Authors

545

Institutions

Publications per year

2017–2026

194

Authors

1.4K

  1. RB
    Rajkumar Buyya

    The University of Melbourne, Australia

    7 publications · 450 citations

    7

    h-index

  2. JR
    Jerry Rolia

    Hewlett-Packard (United States), United States

    6 publications · 529 citations

    6

    h-index

  3. AK
    Ashutosh Kumar Singh

    National Institute of Technology Kurukshetra, India

    6 publications · 330 citations

    6

    h-index

  4. JK
    Jitendra Kumar

    National Institute of Technology Kurukshetra, India

    5 publications · 297 citations

    5

    h-index

  5. GC
    Giuliano Casale

    Imperial College London, United Kingdom

    5 publications · 216 citations

    5

    h-index

1–5 of 1.4K

Venues

  1. IEEE Access

    8 publications · 260 citations

    8

    h-index

  2. arXiv (Cornell University)

    Repository · 6 publications · 140 citations

    7

    h-index

  3. Future Generation Computer Systems

    FGCS · 7 publications · 370 citations

    7

    h-index

  4. IEEE Transactions on Cloud Computing

    7 publications · 350 citations

    7

    h-index

  5. The Journal of Supercomputing

    6 publications · 262 citations

    6

    h-index

1–5