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dc.contributor.authorCuadra Gómez, Julen
dc.contributor.authorHurtado Pando, Ekaitz
dc.contributor.authorSarachaga González, María Isabel ORCID
dc.contributor.authorEstévez Estévez, Elisabet
dc.contributor.authorCasquero Oyarzabal, Oscar ORCID
dc.contributor.authorArmentia Díaz de Tuesta, Aintzane ORCID
dc.date.accessioned2024-04-16T16:26:15Z
dc.date.available2024-04-16T16:26:15Z
dc.date.issued2024-04-08
dc.identifier.citationFuture Generation Computer Systems 157 : 360-375 (2024)es_ES
dc.identifier.issn0167-739X
dc.identifier.issn1872-7115
dc.identifier.urihttp://hdl.handle.net/10810/66718
dc.description.abstractCloud Computing is revolutionizing smart manufacturing by offering on-demand and scalable computer systems that facilitate plant data analysis and operational efficiency optimization. DevOps is a methodology, widely used for developing Cloud Computing systems, that streamlines software development by improving its integration, delivery, and deployment. Although cloud application designers within a DevOps team are assumed to have development and operational knowledge, this does not fall within the skills of experts that design analytics applications of plant data. The deployment environment is also relevant since, as such applications are often hosted in the Fog, the proliferation of application components may hinder their composition and validation. This work is aimed at embracing the Platform Engineering approach to provide a tailored toolkit that guides the design and development of OpenFog compliant applications for the experts in the Smart Manufacturing domain. The platform uses Model Driven Engineering techniques and a flow-based visual editor to allow application designers to graphically compose applications from components previously delivered by component developers, abstracting them from the underlying technologies. As a result, containerized applications, ready to be deployed and run by a container orchestrator, are obtained. The feasibility of the proposal is proved through an industrial case study.es_ES
dc.description.sponsorshipThis work was financed by the project RTI2018-096116-B-I00 funded by MCIN/AEI/10.13039/501100011033/ and funded by ERDF A way of making Europe, by the project PES18/48 funded by UPV/EHU, by Open Access funding provided by University of Basque Country, Spain and by the PhD fellowship granted under the frame of the PIF 2022 call funded by the University of the Basque Country (UPV/EHU), Spain, grant number PIF22/188.es_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.relationinfo:eu-repo/grantAgreement/MCIN/RTI2018-096116-B-I00es_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectfog computinges_ES
dc.subjectmodel driven engineeringes_ES
dc.subjectnode-REDes_ES
dc.subjectsmart manufacturinges_ES
dc.subjectDevOpses_ES
dc.subjectplatform engineeringes_ES
dc.titleEnabling DevOps for Fog Applications in the Smart Manufacturing domain: A Model-Driven based Platform Engineering approaches_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.holder© 2024 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY licensees_ES
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S0167739X24001286es_ES
dc.identifier.doi10.1016/j.future.2024.03.053
dc.departamentoesIngeniería de sistemas y automáticaes_ES
dc.departamentoeuSistemen ingeniaritza eta automatikaes_ES


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© 2024 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license
Except where otherwise noted, this item's license is described as © 2024 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license