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Auto-scaling of Containers: The Impact of Relative and Absolute Metrics
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik. (Cloud, Networking and Security)ORCID-id: 0000-0002-3118-5058
Spindox S.p.A, ITA.
2017 (engelsk)Inngår i: 2017 IEEE 2nd International Workshops on Foundations and Applications of Self* Systems, FAS*W 2017 / [ed] IEEE, IEEE, 2017, s. 207-214, artikkel-id 8064125Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Today, The cloud industry is adopting the container technology both for internal usage and as commercial offering. The use of containers as base technology for large-scale systems opens many challenges in the area of resource management at run-time. This paper addresses the problem of selecting the more appropriate performance metrics to activate auto-scaling actions. Specifically, we investigate the use of relative and absolute metrics. Results demonstrate that, for CPU intense workload, the use of absolute metrics enables more accurate scaling decisions. We propose and evaluate the performance of a new autoscaling algorithm that could reduce the response time of a factor between 0.66 and 0.5 compared to the actual Kubernetes' horizontal auto-scaling algorithm.

sted, utgiver, år, opplag, sider
IEEE, 2017. s. 207-214, artikkel-id 8064125
Emneord [en]
Containers, Measurement, Time factors, Correlation, Probability density function, Resource management, Benchmark testing
HSV kategori
Identifikatorer
URN: urn:nbn:se:bth-15623DOI: 10.1109/FAS-W.2017.149ISI: 000426936100038ISBN: 978-1-5090-6558-5 (digital)ISBN: 978-1-5090-6559-2 (tryckt)OAI: oai:DiVA.org:bth-15623DiVA, id: diva2:1164121
Konferanse
2nd IEEE International Workshops on Foundations and Applications of Self* Systems, FAS*W, Tucson
Prosjekter
Scalable resource-efficient systems for big data analytics - BigData@BTH
Forskningsfinansiär
Knowledge Foundation, 20140032Tilgjengelig fra: 2017-12-10 Laget: 2017-12-10 Sist oppdatert: 2018-04-04bibliografisk kontrollert

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