Searched for: subject%3A%22autoscaling%22
(1 - 4 of 4)
document
Siachamis, G. (author), Kanis, Job (author), Koper, Wybe (author), Psarakis, K. (author), Fragkoulis, M. (author), van Deursen, A. (author), Katsifodimos, A (author)
In this work, we evaluate autoscaling solutions for stream processing engines. Although autoscaling has become a mainstream subject of research in the last decade, the database research community has yet to evaluate different autoscaling techniques under a proper benchmarking setting and evaluation framework. As a result, every newly proposed...
conference paper 2023
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Ilyushkin, A.S. (author)
This dissertation addresses three key challenges that are characteristic to the online scheduling of workloads of workflows in modern distributed computing systems. The first challenge is the realistic estimation of the resource demand of a workflow, as it is important for making good task placement and resource allocation decisions. Usually,...
doctoral thesis 2019
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Ilyushkin, A.S. (author), Ali-Eldin, Ahmed (author), Herbst, Nikolas (author), Bauer, André (author), Papadopoulos, Alessandro (author), Epema, D.H.J. (author), Iosup, A. (author)
Elasticity is one of the main features of cloud computing allowing customers to scale their resources based on the workload. Many autoscalers have been proposed in the past decade to decide on behalf of cloud customers when and how to provision resources to a cloud application based on the workload utilizing cloud elasticity features. However,...
journal article 2018
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Ali-Eldin, Ahmed (author), Ilyushkin, A.S. (author), Ghit, B.I. (author), Herbst, Nikolas (author), Papadopoulos, Alessandro (author), Iosup, A. (author)
Rapid elasticity is one of the essential characteristics of cloud computing identified by NIST. Elasticity allows resources to be provisioned and released to scale rapidly out ward and in ward according to demand. Tens -- if not hundreds -- of algorithms have been proposed in the literature to automatically achieve elastic provisioning. These...
conference paper 2016
Searched for: subject%3A%22autoscaling%22
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