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Holistic workload scaling : A new approach to compute acceleration in the cloud

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dc.creatorPérez, Juan F.
dc.creatorChen, Lydia Y.
dc.creatorVillari, Massimo
dc.creatorRanjan, Rajiv
dc.creator.googlePérez, Juan F.spa
dc.creator.googleChen, Lydia Y.
dc.creator.googleVillari, Massimo
dc.creator.googleRanjan, Rajiv
dc.date.accessioned2019-02-15T19:41:21Z
dc.date.available2019-02-15T19:41:21Z
dc.date.created2018
dc.date.issued2018
dc.description.abstractWorkload scaling is an approach to accelerating computation and thus improving response times by replicating the exact same request multiple times and processing it in parallel on multiple nodes and accepting the result from the first node to finish. This is not unlike a TV game show, where the same question is given to multiple contestants and the (correct) answer is accepted from the first to respond. This is different than traditional strategies for parallelization as used in, say, MapReduce workloads, where each node runs a subset of the overall workload. There are a variety of strategies that trade off metrics such as cost, utilization, performance, and interprocessor communication requirements. Performance modeling can help determine optimal approaches for different environments and goals. This is important, because poor performance can lead to application and domain-specific losses, such as e-commerce conversions and sales. Performance modeling and analysis plays an important role in designing and driving the selection of resource scaling mechanisms. Such modeling and analysis is complex due to time-varying workload arrival rates and request sizes, and even more complex in cloud environments due to the additional stochastic variation caused by performance interference due to resource sharing across co-located tenants. Moreover, little is known on how to multi-scale, i.e., dynamically and simultaneously scale resources vertically, horizontally, and through workload scaling. In this article, we first demonstrate the effectiveness of multi-scaling in reducing latency, and then discuss the performance modeling challenges, particularly for workload scaling. © 2014 IEEE.eng
dc.format.mimetypeapplication/pdf
dc.identifier.doi10.1109/MCC.2018.011791711
dc.identifier.issn2325-6095
dc.identifier.urihttp://repository.urosario.edu.co/handle/10336/19089
dc.language.isoengspa
dc.relation.citationEndPage30
dc.relation.citationStartPage20
dc.relation.citationTitleIEEE Cloud Computing
dc.relation.citationVolumeVol. 5
dc.relation.ispartofIEEE Cloud Computing, ISSN:2325-6095, Vol. 5 (2018) pp. 20-30spa
dc.relation.urihttps://www.computer.org/csdl/mags/cd/2018/01/mcd2018010020.pdfspa
dc.rights.accesRightsinfo:eu-repo/semantics/openAccess
dc.rights.accesoAbierto (Texto Completo)spa
dc.source.bibliographicCitationMetrics, K., Blog, , https://blog.kissmetrics.com/loading-timespa
dc.source.instnameinstname:Universidad del Rosario
dc.source.reponamereponame:Repositorio Institucional EdocUR
dc.subjectCloud Computingspa
dc.subjectStochastic Systemsspa
dc.subjectCloud Environmentsspa
dc.subjectInter Processor Communicationspa
dc.subjectMapreudcespa
dc.subjectModel And Analysisspa
dc.subjectOptimal Approachesspa
dc.subjectParallelilzationspa
dc.subjectPerformance Modeling And Analysisspa
dc.subjectStochastic Variationspa
dc.subjectEconomic And Social Effectsspa
dc.subject.ddcProbabilidades & matemáticas aplicadasspa
dc.subject.lembSistemas estocásticosspa
dc.subject.lembComercio electrónicospa
dc.titleHolistic workload scaling : A new approach to compute acceleration in the cloudspa
dc.typearticleeng
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersion
dc.type.spaArtículospa
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