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dc.contributor.authorPodolskiy, Vladimiren_NZ
dc.contributor.authorMayo, Michaelen_NZ
dc.contributor.authorKoay, Abigailen_NZ
dc.contributor.authorGerndt, Michaelen_NZ
dc.contributor.authorPatros, Panagiotisen_NZ
dc.coverage.spatialUmeå, Swedenen_NZ
dc.date.accessioned2019-09-19T00:18:45Z
dc.date.available2019en_NZ
dc.date.available2019-09-19T00:18:45Z
dc.date.issued2019en_NZ
dc.identifier.citationPodolskiy, V., Mayo, M., Koay, A., Gerndt, M., & Patros, P. (2019). Maintaining SLOs of cloud-native applications via self-adaptive resource sharing. In Proc 13th IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2019) (pp. 72–81). Washington, DC, USA: IEEE. https://doi.org/10.1109/SASO.2019.00018en
dc.identifier.urihttps://hdl.handle.net/10289/12888
dc.description.abstractWith changing workloads, cloud service providers can leverage vertical container scaling (adding/removing resources) so that Service Level Objective (SLO) violations are minimized and spare resources are maximized. In this paper, we investigate a solution to the self-adaptive problem of vertical elasticity for co-located containerized applications. First, the system learns performance models that relate SLOs to workload, resource limits and service level indicators. Second, it derives limits that meet SLOs and minimize resource consumption via a combination of optimization and restricted brute-force search. Third, it vertically scales containers based on the derived limits. We evaluated our technique on a Kubernetes private cloud of 8 nodes with three deployed applications. The results registered two SLO violations out of 16 validation tests; acceptably low derivation times facilitate realistic deployment. Violations are primarily attributed to application specifics, such as garbage collection, which require further research to be circumvented.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherIEEEen_NZ
dc.rightsThis is an author’s accepted version of an article published in the Proceedings of 13th IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2019). © 2019 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
dc.subjectcomputer scienceen_NZ
dc.subjectCloud computingen_NZ
dc.subjectcontainersen_NZ
dc.subjectpredictive modelsen_NZ
dc.subjectresource managementen_NZ
dc.subjectoptimizationen_NZ
dc.subjectadaptation modelsen_NZ
dc.subjectMachine learning
dc.titleMaintaining SLOs of cloud-native applications via self-adaptive resource sharingen_NZ
dc.typeConference Contribution
dc.identifier.doi10.1109/SASO.2019.00018en_NZ
dc.relation.isPartOfProc 13th IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2019)en_NZ
pubs.begin-page72
pubs.elements-id237174
pubs.end-page81
pubs.finish-date2019-06-20en_NZ
pubs.place-of-publicationWashington, DC, USA
pubs.start-date2019-06-16en_NZ


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