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
Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/127482
Title:
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A Self-managed Mesos Cluster for Data Analytics with QoS Guarantees
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Author:
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López-Huguet, Sergio
Pérez-González, Alfonso María
Calatrava Arroyo, Amanda
Alfonso Laguna, Carlos De
Caballer Fernández, Miguel
Moltó, Germán
Blanquer Espert, Ignacio
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UPV Unit:
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Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació
Universitat Politècnica de València. Instituto de Instrumentación para Imagen Molecular - Institut d'Instrumentació per a Imatge Molecular
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Issued date:
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Abstract:
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[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 ...[+]
[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.
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Subjects:
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Cloud orchestration
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Elasticity
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Quality of service
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Data analytics
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Hybrid clusters
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Copyrigths:
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Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
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Source:
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Future Generation Computer Systems. (issn:
0167-739X
)
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DOI:
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10.1016/j.future.2019.02.047
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Publisher:
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Elsevier
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Publisher version:
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http://doi.org/10.1016/j.future.2019.02.047
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Project ID:
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info:eu-repo/grantAgreement/MINECO//TIN2016-79951-R/ES/COMPUTACION BIG DATA Y DE ALTAS PRESTACIONES SOBRE MULTI-CLOUDS ELASTICOS/
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Thanks:
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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 ...[+]
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.
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Type:
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Artículo
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