Large-scale IoT network offloading to cloud and fog computing : A fluid limit model

Belcredi, Gonzalo - Aspirot, Laura - Monzón, Pablo - Belzarena, Pablo

Resumen:

This paper models a large-scale Internet of Things (IoT) network as a stochastic system that offloads computing towards Fog and Cloud via a shared access medium. The analysis of this large IoT system by stochastic methods is a challenging problem, if possible, to solve. This paper proposes the approximation of the dynamic of the IoT network via the fluid limit of the stochastic process. This method allows the analysis of the large-scale system and also allows finding the equilibrium point of the system. The results obtained with stochastic simulations show that the fluid model is an excellent approximation of the stochastic system.


Detalles Bibliográficos
2021
Beca de Maestría ANII
Cloud computing
Computational modeling
Stochastic systems
Fluid dynamics
Stochastic processes
Large-scale systems
Internet of Things
Edge Computing
Fog Computing
Computation Offloading
Markov Process
Fluid Limit
Switched Systems
Wireless Communications
Inglés
Universidad de la República
COLIBRI
https://ieeexplore.ieee.org/document/9647124
https://hdl.handle.net/20.500.12008/34967
Acceso abierto
Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)
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author Belcredi, Gonzalo
author2 Aspirot, Laura
Monzón, Pablo
Belzarena, Pablo
author2_role author
author
author
author_facet Belcredi, Gonzalo
Aspirot, Laura
Monzón, Pablo
Belzarena, Pablo
author_role author
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collection COLIBRI
dc.contributor.filiacion.none.fl_str_mv Belcredi Gonzalo, Universidad de la República (Uruguay). Facultad de Ingeniería.
Aspirot Laura, Universidad de la República (Uruguay). Facultad de Ciencias Económicas y de Administración
Monzón Pablo, Universidad de la República (Uruguay). Facultad de Ingeniería.
Belzarena Pablo, Universidad de la República (Uruguay). Facultad de Ingeniería.
dc.creator.none.fl_str_mv Belcredi, Gonzalo
Aspirot, Laura
Monzón, Pablo
Belzarena, Pablo
dc.date.accessioned.none.fl_str_mv 2022-11-23T13:24:39Z
dc.date.available.none.fl_str_mv 2022-11-23T13:24:39Z
dc.date.issued.none.fl_str_mv 2021
dc.description.abstract.none.fl_txt_mv This paper models a large-scale Internet of Things (IoT) network as a stochastic system that offloads computing towards Fog and Cloud via a shared access medium. The analysis of this large IoT system by stochastic methods is a challenging problem, if possible, to solve. This paper proposes the approximation of the dynamic of the IoT network via the fluid limit of the stochastic process. This method allows the analysis of the large-scale system and also allows finding the equilibrium point of the system. The results obtained with stochastic simulations show that the fluid model is an excellent approximation of the stochastic system.
dc.description.es.fl_txt_mv Presentado y publicado en 2021 IEEE URUCON, Montevideo, Uruguay, 24-26 nov. 2021, pp. 377-381.
dc.description.sponsorship.none.fl_txt_mv Beca de Maestría ANII
dc.format.extent.es.fl_str_mv 5 p.
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dc.identifier.citation.es.fl_str_mv Belcredi, G., Aspirot, L., Monzón, P. y otros. Large-scale IoT network offloading to cloud and fog computing : A fluid limit model [Preprint]. Publicado en: 2021 IEEE URUCON, Montevideo, Uruguay, 24-26 nov 2021, pp. 377-381. DOI 10.1109/URUCON53396.2021.9647124
dc.identifier.uri.none.fl_str_mv https://ieeexplore.ieee.org/document/9647124
https://hdl.handle.net/20.500.12008/34967
dc.language.iso.none.fl_str_mv en
eng
dc.rights.license.none.fl_str_mv Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
dc.source.none.fl_str_mv reponame:COLIBRI
instname:Universidad de la República
instacron:Universidad de la República
dc.subject.es.fl_str_mv Cloud computing
Computational modeling
Stochastic systems
Fluid dynamics
Stochastic processes
Large-scale systems
Internet of Things
Edge Computing
Fog Computing
Computation Offloading
Markov Process
Fluid Limit
Switched Systems
Wireless Communications
dc.title.none.fl_str_mv Large-scale IoT network offloading to cloud and fog computing : A fluid limit model
dc.type.es.fl_str_mv Preprint
dc.type.none.fl_str_mv info:eu-repo/semantics/preprint
dc.type.version.none.fl_str_mv info:eu-repo/semantics/submittedVersion
description Presentado y publicado en 2021 IEEE URUCON, Montevideo, Uruguay, 24-26 nov. 2021, pp. 377-381.
eu_rights_str_mv openAccess
format preprint
id COLIBRI_b373b75c5bb9217a4f3ccd022b2989c9
identifier_str_mv Belcredi, G., Aspirot, L., Monzón, P. y otros. Large-scale IoT network offloading to cloud and fog computing : A fluid limit model [Preprint]. Publicado en: 2021 IEEE URUCON, Montevideo, Uruguay, 24-26 nov 2021, pp. 377-381. DOI 10.1109/URUCON53396.2021.9647124
instacron_str Universidad de la República
institution Universidad de la República
instname_str Universidad de la República
language eng
language_invalid_str_mv en
network_acronym_str COLIBRI
network_name_str COLIBRI
oai_identifier_str oai:colibri.udelar.edu.uy:20.500.12008/34967
publishDate 2021
reponame_str COLIBRI
repository.mail.fl_str_mv mabel.seroubian@seciu.edu.uy
repository.name.fl_str_mv COLIBRI - Universidad de la República
repository_id_str 4771
rights_invalid_str_mv Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)
spelling Belcredi Gonzalo, Universidad de la República (Uruguay). Facultad de Ingeniería.Aspirot Laura, Universidad de la República (Uruguay). Facultad de Ciencias Económicas y de AdministraciónMonzón Pablo, Universidad de la República (Uruguay). Facultad de Ingeniería.Belzarena Pablo, Universidad de la República (Uruguay). Facultad de Ingeniería.2022-11-23T13:24:39Z2022-11-23T13:24:39Z2021Belcredi, G., Aspirot, L., Monzón, P. y otros. Large-scale IoT network offloading to cloud and fog computing : A fluid limit model [Preprint]. Publicado en: 2021 IEEE URUCON, Montevideo, Uruguay, 24-26 nov 2021, pp. 377-381. DOI 10.1109/URUCON53396.2021.9647124https://ieeexplore.ieee.org/document/9647124https://hdl.handle.net/20.500.12008/34967Presentado y publicado en 2021 IEEE URUCON, Montevideo, Uruguay, 24-26 nov. 2021, pp. 377-381.This paper models a large-scale Internet of Things (IoT) network as a stochastic system that offloads computing towards Fog and Cloud via a shared access medium. The analysis of this large IoT system by stochastic methods is a challenging problem, if possible, to solve. This paper proposes the approximation of the dynamic of the IoT network via the fluid limit of the stochastic process. This method allows the analysis of the large-scale system and also allows finding the equilibrium point of the system. The results obtained with stochastic simulations show that the fluid model is an excellent approximation of the stochastic system.Submitted by Ribeiro Jorge (jribeiro@fing.edu.uy) on 2022-11-22T05:49:40Z No. of bitstreams: 2 license_rdf: 23149 bytes, checksum: 1996b8461bc290aef6a27d78c67b6b52 (MD5) BAMB21.pdf: 3015830 bytes, checksum: f952fa0b79e70da89036df0dcf0ddc2a (MD5)Approved for entry into archive by Machado Jimena (jmachado@fing.edu.uy) on 2022-11-22T19:28:31Z (GMT) No. of bitstreams: 2 license_rdf: 23149 bytes, checksum: 1996b8461bc290aef6a27d78c67b6b52 (MD5) BAMB21.pdf: 3015830 bytes, checksum: f952fa0b79e70da89036df0dcf0ddc2a (MD5)Made available in DSpace by Luna Fabiana (fabiana.luna@seciu.edu.uy) on 2022-11-23T13:24:39Z (GMT). No. of bitstreams: 2 license_rdf: 23149 bytes, checksum: 1996b8461bc290aef6a27d78c67b6b52 (MD5) BAMB21.pdf: 3015830 bytes, checksum: f952fa0b79e70da89036df0dcf0ddc2a (MD5) Previous issue date: 2021Beca de Maestría ANII5 p.application/pdfenengLas obras depositadas en el Repositorio se rigen por la Ordenanza de los Derechos de la Propiedad Intelectual de la Universidad de la República.(Res. Nº 91 de C.D.C. de 8/III/1994 – D.O. 7/IV/1994) y por la Ordenanza del Repositorio Abierto de la Universidad de la República (Res. Nº 16 de C.D.C. de 07/10/2014)info:eu-repo/semantics/openAccessLicencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)Cloud computingComputational modelingStochastic systemsFluid dynamicsStochastic processesLarge-scale systemsInternet of ThingsEdge ComputingFog ComputingComputation OffloadingMarkov ProcessFluid LimitSwitched SystemsWireless CommunicationsLarge-scale IoT network offloading to cloud and fog computing : A fluid limit modelPreprintinfo:eu-repo/semantics/preprintinfo:eu-repo/semantics/submittedVersionreponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaBelcredi, GonzaloAspirot, LauraMonzón, PabloBelzarena, PabloSistemas y ControlTelecomunicacionesAnálisis de Redes, Tráfico y Estadísticas de ServiciosAnálisis de Redes, Tráfico y Estadísticas de ServiciosLICENSElicense.txtlicense.txttext/plain; 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- Universidad de la Repúblicafalse
spellingShingle Large-scale IoT network offloading to cloud and fog computing : A fluid limit model
Belcredi, Gonzalo
Cloud computing
Computational modeling
Stochastic systems
Fluid dynamics
Stochastic processes
Large-scale systems
Internet of Things
Edge Computing
Fog Computing
Computation Offloading
Markov Process
Fluid Limit
Switched Systems
Wireless Communications
status_str submittedVersion
title Large-scale IoT network offloading to cloud and fog computing : A fluid limit model
title_full Large-scale IoT network offloading to cloud and fog computing : A fluid limit model
title_fullStr Large-scale IoT network offloading to cloud and fog computing : A fluid limit model
title_full_unstemmed Large-scale IoT network offloading to cloud and fog computing : A fluid limit model
title_short Large-scale IoT network offloading to cloud and fog computing : A fluid limit model
title_sort Large-scale IoT network offloading to cloud and fog computing : A fluid limit model
topic Cloud computing
Computational modeling
Stochastic systems
Fluid dynamics
Stochastic processes
Large-scale systems
Internet of Things
Edge Computing
Fog Computing
Computation Offloading
Markov Process
Fluid Limit
Switched Systems
Wireless Communications
url https://ieeexplore.ieee.org/document/9647124
https://hdl.handle.net/20.500.12008/34967