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Dual Scaling VMs and Queries: Cost-Effective Latency Curtailment

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dc.creatorPérez, Juan F.spa
dc.creatorBirke R.spa
dc.creatorBjorkqvist M.spa
dc.creatorChen L.Y.spa
dc.date.accessioned2020-05-25T23:58:26Z
dc.date.available2020-05-25T23:58:26Z
dc.date.created2017spa
dc.description.abstractWimpy virtual instances equipped with small numbers of cores and RAM are popular public and private cloud offerings because of their low cost for hosting applications. The challenge is how to run latency-sensitive applications using such instances, which trade off performance for cost. In this study, we analytically and experimentally show that simultaneously scaling resources at coarse granularity and workloads, i.e., submitting multiple query clones to different servers, at fine granularity can overcome the performance disadvantages of wimpy VM instances and achieve stringent latency targets that are even lower than the average execution times of wimpy servers. To such an end, we first derive a closed-form analysis for the latency under any given VM provisioning and query replication level, considering cloning policies that can (not) terminate outstanding clones with (without) an overhead. Validated on trace-driven simulations, our analysis is able to accurately predict the latency and efficiently search for the optimal number of VMs and clones. Secondly, we develop a dual elastic scaler, DuoScale, that dynamically scales VMs and clones according to the workload dynamics so as to achieve the target latency in a cost-effective manner. The effectiveness of DuoScale lies on the observation that the application performance only scales sub-linearly with increasing vertical or horizontal resource provisioning, i.e., resources per VM or number of VMs. We evaluate DuoScale against VM-only scaling strategies via extensive trace-driven simulations as well as experimental results on a cloud test-bed. Our results show that DuoScale is able to achieve the stringent target latency by using clones on wimpy VMs with cost savings up to 50%, compared to scaling brawny VMs that have better performance at a higher unit cost. © 2017 IEEE.eng
dc.format.mimetypeapplication/pdf
dc.identifier.doihttps://doi.org/10.1109/ICDCS.2017.231
dc.identifier.issn2016
dc.identifier.urihttps://repository.urosario.edu.co/handle/10336/22864
dc.language.isoengspa
dc.publisherInstitute of Electrical and Electronics Engineers Inc.spa
dc.relation.citationEndPage998
dc.relation.citationStartPage988
dc.relation.citationTitleProceedings - International Conference on Distributed Computing Systems
dc.relation.ispartofProceedings - International Conference on Distributed Computing Systems, ISSN:2016,(2017); pp. 988-998spa
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85027252830&doi=10.1109%2fICDCS.2017.231&partnerID=40&md5=8d14657ed2f91f9a62f278a6c681a59cspa
dc.rights.accesRightsinfo:eu-repo/semantics/openAccess
dc.rights.accesoAbierto (Texto Completo)spa
dc.source.instnameinstname:Universidad del Rosariospa
dc.source.reponamereponame:Repositorio Institucional EdocURspa
dc.subject.keywordCloningspa
dc.subject.keywordCost effectivenessspa
dc.subject.keywordCostsspa
dc.subject.keywordEconomic and social effectsspa
dc.subject.keywordApplication performancespa
dc.subject.keywordClosed-form analysisspa
dc.subject.keywordFine granularityspa
dc.subject.keywordMultiple queriesspa
dc.subject.keywordQuery replicationsspa
dc.subject.keywordSensitive applicationspa
dc.subject.keywordTrace driven simulationspa
dc.subject.keywordVM provisioningspa
dc.subject.keywordDistributed computer systemsspa
dc.titleDual Scaling VMs and Queries: Cost-Effective Latency Curtailmentspa
dc.typeconferenceObjecteng
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersion
dc.type.spaDocumento de conferenciaspa
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