Adaptive Scaling

Adaptive Scaling is a dynamic resource allocation framework that distinguishes itself by automatically adjusting system capacity in response to real-time workload fluctuations to optimize throughput in computing environments.

196

Publications

8K

Citations

646

Authors

252

Institutions

Publications per year

2017–2026

81

Authors

646

  1. AB
    Andrew B. Kahng

    University of California San Diego, United States

    4 publications · 48 citations

    4

    h-index

  2. TC
    Tuck-Boon Chan

    University of California San Diego, United States

    4 publications · 48 citations

    4

    h-index

  3. WJ
    Wei-Ting Jonas Chan

    University of California San Diego, United States

    3 publications · 31 citations

    3

    h-index

  4. BG
    Boris Ginsburg

    Nvidia (United States), United States

    3 publications · 751 citations

    3

    h-index

  5. DP
    Dmitry Ponomarev

    Binghamton University, United States

    3 publications · 265 citations

    3

    h-index

1–5 of 646

Venues

  1. IEEE Journal of Solid-State Circuits

    4 publications · 201 citations

    4

    h-index

  2. arXiv (Cornell University)

    Repository · 4 publications · 794 citations

    4

    h-index

  3. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 3 publications · 54 citations

    3

    h-index

  4. IEEE Transactions on Computers

    3 publications · 74 citations

    3

    h-index

  5. IEEE Transactions on Parallel and Distributed Systems

    TPDS · 3 publications · 70 citations

    3

    h-index

1–5