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SmallTail: Scaling cores and probabilistic cloning requests for web systems

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dc.creatorLakew E.B.spa
dc.creatorBirke R.spa
dc.creatorPérez, Juan F.spa
dc.creatorElmroth E.spa
dc.creatorChen L.Y.spa
dc.date.accessioned2020-05-25T23:58:24Z
dc.date.available2020-05-25T23:58:24Z
dc.date.created2018spa
dc.description.abstractUsers quality of experience on web systems are largely determined by the tail latency, e.g., 95th percentile. Scaling resources along, e.g., the number of virtual cores per VM, is shown to be effective to meet the average latency but falls short in taming the latency tail in the cloud where the performance variability is higher. The prior art shows the prominence of increasing the request redundancy to curtail the latency either in the off-line setting or without scaling-in cores of virtual machines. In this paper, we propose an opportunistic scaler, termed SmallTail, which aims to achieve stringent targets of tail latency while provisioning a minimum amount of resources and keeping them well utilized. Against dynamic workloads, SmallTail simultaneously adjusts the core provisioning per VM and probabilistically replicates requests so as to achieve the tail latency target. The core of SmallTail is a two level controller, where the outer loops controls the core provision per distributed VMs and the inner loop controls the clones in a finer granularity. We also provide theoretical analysis on the steady-state latency for a given probabilistic replication that clones one out of N arriving requests. We extensively evaluate SmallTail on three different web systems, namely web commerce, web searching, and web bulletin board. Our testbed results show that SmallTail can ensure the 95th latency below 1000 ms using up to 53% less cores compared to the strategy of constant cloning, whereas scaling-core only solution exceeds the latency target by up to 70%. © 2018 IEEE.eng
dc.format.mimetypeapplication/pdf
dc.identifier.doihttps://doi.org/10.1109/ICAC.2018.00013
dc.identifier.urihttps://repository.urosario.edu.co/handle/10336/22856
dc.language.isoengspa
dc.publisherInstitute of Electrical and Electronics Engineers Inc.spa
dc.relation.citationEndPage40
dc.relation.citationStartPage31
dc.relation.citationTitleProceedings - 15th IEEE International Conference on Autonomic Computing
dc.relation.ispartofProceedings - 15th IEEE International Conference on Autonomic Computing, ICAC 2018,(2018); pp. 31-40spa
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85061316455&doi=10.1109%2fICAC.2018.00013&partnerID=40&md5=87c34c798122078edea62f43188634cdspa
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.keywordControllersspa
dc.subject.keywordLevel controlspa
dc.subject.keywordQuality of servicespa
dc.subject.keywordVirtual machinespa
dc.subject.keywordWebsitesspa
dc.subject.keywordAutoscalingspa
dc.subject.keywordInner loop controlsspa
dc.subject.keywordLatency controlspa
dc.subject.keywordLevel controllersspa
dc.subject.keywordPerformance variabilityspa
dc.subject.keywordProbabilistic cloningspa
dc.subject.keywordProbablisticspa
dc.subject.keywordQuality of experience (qoe)spa
dc.subject.keywordCloningspa
dc.subject.keywordProbablistic cloningspa
dc.subject.keywordQuery cloningspa
dc.subject.keywordTail latency controlspa
dc.subject.keywordTwo level controllerspa
dc.subject.keywordVertical autoscalingspa
dc.titleSmallTail: Scaling cores and probabilistic cloning requests for web systemsspa
dc.typeconferenceObjecteng
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersion
dc.type.spaDocumento de conferenciaspa
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