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KPI-agnostic Control for Fine-Grained Vertical Elasticity
Umeå University, Faculty of Science and Technology, Department of Computing Science.
Umeå University, Faculty of Science and Technology, Department of Computing Science.
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2017 (English)In: 2017 17TH IEEE/ACM INTERNATIONAL SYMPOSIUM ON CLUSTER, CLOUD AND GRID COMPUTING (CCGRID), IEEE , 2017, p. 589-598Conference paper, Published paper (Refereed)
Abstract [en]

Applications hosted in the cloud have become indispensable in several contexts, with their performance often being key to business operation and their running costs needing to be minimized. To minimize running costs, most modern virtualization technologies such as Linux Containers, Xen, and KVM offer powerful resource control primitives for individual provisioning - that enable adding or removing of fraction of cores and/or megabytes of memory for as short as few seconds. Despite the technology being ready, there is a lack of proper techniques for fine-grained resource allocation, because there is an inherent challenge in determining the correct composition of resources an application needs, with varying workload, to ensure deterministic performance.

This paper presents a control-based approach for the management of multiple resources, accounting for the resource consumption, together with the application performance, enabling fine-grained vertical elasticity. The control strategy ensures that the application meets the target performance indicators, consuming as less resources as possible. We carried out an extensive set of experiments using different applications – interactive with response-time requirements, as well as non-interactive with throughput desires – by varying the workload mixes of each application over time. The results demonstrate that our solution precisely provides guaranteed performance while at the same time avoiding both resource over- and under-provisioning.

Place, publisher, year, edition, pages
IEEE , 2017. p. 589-598
Series
IEEE-ACM International Symposium on Cluster Cloud and Grid Computing, ISSN 2376-4414
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:umu:diva-146250DOI: 10.1109/CCGRID.2017.71ISI: 000426912900063ISBN: 978-1-5090-6611-7 (print)OAI: oai:DiVA.org:umu-146250DiVA, id: diva2:1206378
Conference
17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), MAY 14-17, 2017, Madrid, SPAIN
Available from: 2018-05-16 Created: 2018-05-16 Last updated: 2018-06-09Bibliographically approved

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Lakew, Ewnetu BayuhPapadopoulos, Alessandro VittorioKlein, CristianElmroth, Erik

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  • apa
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