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Multi-objective scheduling of extreme data scientific workflows in Fog

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carlosiii.embargo.terms2022-05-01
dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Arquitectura de Computadores, Comunicaciones y Sistemases
dc.contributor.authorDe Maio, Vincenzo
dc.contributor.authorKimovski, Dragi
dc.contributor.funderEuropean Commissiones
dc.date.accessioned2020-04-22T12:30:07Z
dc.date.available2022-05-01T23:00:06Z
dc.date.issued2020-05
dc.descriptionASPIDE: Exascale programIng models for extreme data processing
dc.description.abstractThe concept of “extreme data” is a recent re-incarnation of the “big data” problem, which is distinguished by the massive amounts of information that must be analyzed with strict time requirements. In the past decade, the Cloud data centers have been envisioned as the essential computing architectures for enabling extreme data workflows. However, the Cloud data centers are often geographically distributed. Such geographical distribution increases offloading latency, making it unsuitable for processing of workflows with strict latency requirements, as the data transfer times could be very high. Fog computing emerged as a promising solution to this issue, as it allows partial workflow processing in lower-network layers. Performing data processing on the Fog significantly reduces data transfer latency, allowing to meet the workflows’ strict latency requirements. However, the Fog layer is highly heterogeneous and loosely connected, which affects reliability and response time of task offloading. In this work, we investigate the potential of Fog for scheduling of extreme data workflows with strict response time requirements. Moreover, we propose a novel Pareto-based approach for task offloading in Fog, called Multi-objective Workflow Offloading (MOWO). MOWO considers three optimization objectives, namely response time, reliability, and financial cost. We evaluate MOWO workflow scheduler on a set of real-world biomedical, meteorological and astronomy workflows representing examples of extreme data application with strict latency requirements.en
dc.description.sponsorshipThe work described in this paper has been funded throughthe Rucon project (Runtime Control in Multi Clouds), Austria, FWF Y 904 START-Programme 2015, Austria, the H2020 ASPIDE project, European Commision, grant agreement No 801091 and the ATOMICFOG project (Austria), OEAD AT/MK 08/2018en
dc.format.extent14es
dc.identifier.bibliographicCitationFuture generation computer systems, 106, Pp. 171-184en
dc.identifier.doihttps://doi.org/10.1016/j.future.2019.12.054
dc.identifier.issn0167-739X
dc.identifier.publicationfirstpage171es
dc.identifier.publicationlastpage184es
dc.identifier.publicationtitleFuture generation computer systemsen
dc.identifier.publicationvolume106es
dc.identifier.urihttps://hdl.handle.net/10016/30177
dc.language.isoengen
dc.publisherElsevier B.V.en
dc.relation.projectIDinfo:eu-repo/grantAgreement/H2020/801091/ASPIDEen
dc.rights©2020 Elsevier B.V. All rights reserved.en
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.en
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.ecienciaInformáticaes
dc.titleMulti-objective scheduling of extreme data scientific workflows in Fogen
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
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