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A Self-managed Mesos Cluster for Data Analytics with QoS Guarantees

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A Self-managed Mesos Cluster for Data Analytics with QoS Guarantees

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dc.contributor.author López-Huguet, Sergio es_ES
dc.contributor.author Pérez-González, Alfonso María es_ES
dc.contributor.author Calatrava Arroyo, Amanda es_ES
dc.contributor.author Alfonso Laguna, Carlos De es_ES
dc.contributor.author Caballer Fernández, Miguel es_ES
dc.contributor.author Moltó, Germán es_ES
dc.contributor.author Blanquer Espert, Ignacio es_ES
dc.date.accessioned 2019-10-06T20:02:24Z
dc.date.available 2019-10-06T20:02:24Z
dc.date.issued 2019 es_ES
dc.identifier.issn 0167-739X es_ES
dc.identifier.uri http://hdl.handle.net/10251/127482
dc.description.abstract [EN] This article describes the development of an automated configuration of a software platform for Data Analytics that supports horizontal and vertical elasticity to guarantee meeting a specific deadline. It specifies all the components, software dependencies and configurations required to build up the cluster, and analyses the deployment times of different instances, as well as the horizontal and vertical elasticity. The approach followed builds up self-managed hybrid clusters that can deal with different workloads and network requirements. The article describes the structure of the recipes, points out to public repositories where the code is available and discusses the limitations of the approach as well as the results of several experiments. es_ES
dc.description.sponsorship The work presented in this article has been partially funded by a research grant from the regional government of the Comunitat Valenciana (Spain), co-funded by the European Union ERDF funds (European Regional Development Fund) of the Comunitat Valenciana 2014-2020, with reference IDIFEDER/2018/032 (High-Performance Algorithms for the Modelling, Simulation and early Detection of diseases in Personalized Medicine). The authors would also like to thank the Spanish "Ministerio de Economia, Industria y Competitividad" for the project "BigCLOE" with reference number TIN2016-79951-R. es_ES
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation AEI/2018/032 es_ES
dc.relation AEI/TIN2016-79951-R es_ES
dc.relation.ispartof Future Generation Computer Systems es_ES
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject Cloud orchestration es_ES
dc.subject Elasticity es_ES
dc.subject Quality of service es_ES
dc.subject Data analytics es_ES
dc.subject Hybrid clusters es_ES
dc.subject.classification CIENCIAS DE LA COMPUTACION E INTELIGENCIA ARTIFICIAL es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.title A Self-managed Mesos Cluster for Data Analytics with QoS Guarantees es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.future.2019.02.047 es_ES
dc.rights.accessRights Embargado es_ES
dc.date.embargoEndDate 2021-07-01 es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto de Instrumentación para Imagen Molecular - Institut d'Instrumentació per a Imatge Molecular es_ES
dc.description.bibliographicCitation López-Huguet, S.; Pérez-González, AM.; Calatrava Arroyo, A.; Alfonso Laguna, CD.; Caballer Fernández, M.; Moltó, G.; Blanquer Espert, I. (2019). A Self-managed Mesos Cluster for Data Analytics with QoS Guarantees. Future Generation Computer Systems. 96:449-461. https://doi.org/10.1016/j.future.2019.02.047 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://doi.org/10.1016/j.future.2019.02.047 es_ES
dc.description.upvformatpinicio 449 es_ES
dc.description.upvformatpfin 461 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 96 es_ES
dc.relation.pasarela S\368794 es_ES
dc.contributor.funder Generalitat Valenciana es_ES
dc.contributor.funder Agencia Estatal de Investigación es_ES


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