Adaptive Autoscaling

Adaptive Autoscaling is an edge serverless computing mechanism that dynamically adjusts resource allocation and service instance provisioning in response to fluctuating workload demands and environmental constraints through real-time feedback loops and predictive analytics, distinguishing itself from static or threshold-based scaling approaches by its context-aware, continuous optimization of performance, cost, and latency trade-offs.

27

Publications

1.1K

Citations

105

Authors

35

Institutions

Publications per year

2017–2026

20

Authors

105

  1. MG
    Minyi Guo

    Shanghai Jiao Tong University, China

    2 publications · 41 citations

    2

    h-index

  2. SS
    Stelios Sotiriadis

    University of Derby, United Kingdom

    2 publications · 88 citations

    2

    h-index

  3. LZ
    Li Zhang

    IBM (United States), United States

    1 publications · 108 citations

    1

    h-index

  4. DL
    Do‐Young Lee

    Ulsan National Institute of Science and Technology, South Korea

    1 publications · 23 citations

    1

    h-index

  5. SA
    Samuel A. Ajila

    Carleton University, Canada

    1 publications · 51 citations

    1

    h-index

1–5 of 105

Venues

  1. Future Generation Computer Systems

    FGCS · 2 publications · 78 citations

    2

    h-index

  2. Computer Science and Software Engineering

    Conference · 1 publications · 21 citations

    1

    h-index

  3. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 1 publications · 25 citations

    1

    h-index

  4. IEEE Internet of Things Journal

    1 publications · 52 citations

    1

    h-index

  5. Figshare

    Repository · 1 publications · 60 citations

    1

    h-index

1–5