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
Parents
27
Adaptive scaling of Kubernetes pods
David Balla, Csaba Simon, Markosz Maliosz · 2020 · 100 citations
Efficient Cloud Auto-Scaling with SLA Objective Using Q-Learning
Shay Horovitz, Yair Arian · 2018 · 71 citations
Adaptive AI-based auto-scaling for Kubernetes
László Toka, Gergely Dobreff, Balázs Fodor et al. · 2020 · 60 citations
1–5 of 27
105
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1
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Ulsan National Institute of Science and Technology, South Korea
1 publications · 23 citations
1
h-index
1
h-index
1–5 of 105
35

Budapest University of Technology and Economics
Budapest, Hungary
2 h-index
7 publications
540 citations

Shanghai Jiao Tong University
Shanghai, China
2 h-index
10 publications
205 citations

Birkbeck, University of London
London, United Kingdom
2 h-index
4 publications
176 citations

The NorthCap University
Gurugram, India
1 h-index
1 publications
23 citations
Free University of Bozen-Bolzano
Bolzano, Italy
1 h-index
6 publications
72 citations
National Cheng Kung University
Tainan City, Taiwan
1 h-index
1 publications
45 citations
University of California System
Oakland, United States
1 h-index
1 publications
59 citations
Alibaba Group (United States)
San Mateo, United States
1 h-index
3 publications
75 citations
1–8 of 35
FGCS · 2 publications · 78 citations
2
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Conference · 1 publications · 21 citations
1
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Conference · 1 publications · 25 citations
1
h-index
1 publications · 52 citations
1
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Repository · 1 publications · 60 citations
1
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