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A study on performance measures for auto-scaling CPU-intensive containerized applications
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.ORCID iD: 0000-0002-3118-5058
2019 (English)In: Cluster Computing, ISSN 1386-7857, E-ISSN 1573-7543Article in journal (Refereed) Epub ahead of print
Abstract [en]

Autoscaling of containers can leverage performance measures from the different layers of the computational stack. This paper investigate the problem of selecting the most appropriate performance measure to activate auto-scaling actions aiming at guaranteeing QoS constraints. First, the correlation between absolute and relative usage measures and how a resource allocation decision can be influenced by them is analyzed in different workload scenarios. Absolute and relative measures could assume quite different values. The former account for the actual utilization of resources in the host system, while the latter account for the share that each container has of the resources used. Then, the performance of a variant of Kubernetes’ auto-scaling algorithm, that transparently uses the absolute usage measures to scale-in/out containers, is evaluated through a wide set of experiments. Finally, a detailed analysis of the state-of-the-art is presented.

Place, publisher, year, edition, pages
Springer New York LLC , 2019.
Keywords [en]
Auto-scaling, Autonomic computing, Container, Correlation, Docker, Kubernetes, Performance evaluation, Computer networks, Correlation methods, Software engineering, Performance evaluations, Containers
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-17534DOI: 10.1007/s10586-018-02890-1Scopus ID: 2-s2.0-85059669161OAI: oai:DiVA.org:bth-17534DiVA, id: diva2:1282968
Available from: 2019-01-28 Created: 2019-01-28 Last updated: 2019-01-28Bibliographically approved

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Casalicchio, Emiliano

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  • nn-NB
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  • Other locale
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Output format
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