LiveDewStream: A stream processing platform for running in-lab distributed deep learning inferences on smartphone clusters at the edge

Autores
Mateos Diaz, Cristian Maximiliano; Hirsch, Mailén; Toloza, Juan Manuel; Zunino Suarez, Alejandro Octavio
Año de publicación
2022
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Dew computing, an evolution of Fog computing, aims at fulfilling computing needs, such as deep learning applied to object classification, close to where data is originated and using computing resources that include consumer electronic devices such as smartphones. Simulation tools like DewSim aid the study of resource allocation mechanisms for exploiting clusters of smartphones, however, there is a gap w.r.t software tools that allow to perform similar studies over real Dew computing testbeds. We have developed LiveDewStream, an open source project to model executable tasks derived from data streams to be run on real smartphone clusters. The project offers a key functionality missing in other tools: reproducibility of battery-driven Dew experiments. Our major contribution is to provide the community a common in vivo platform to study best-performing allocation mechanisms under different stream processing scenarios and/or deep learning inference models.
Fil: Mateos Diaz, Cristian Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
Fil: Hirsch, Mailén. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
Fil: Toloza, Juan Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
Fil: Zunino Suarez, Alejandro Octavio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
Materia
MOBILE DEVICES
STREAM PROCESSING
DEEP LEARNING
DEW COMPUTING
ANDROID
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/211275
Acceder
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network_name_str CONICET Digital (CONICET)
spelling LiveDewStream: A stream processing platform for running in-lab distributed deep learning inferences on smartphone clusters at the edgeMateos Diaz, Cristian MaximilianoHirsch, MailénToloza, Juan ManuelZunino Suarez, Alejandro OctavioMOBILE DEVICESSTREAM PROCESSINGDEEP LEARNINGDEW COMPUTINGANDROIDhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Dew computing, an evolution of Fog computing, aims at fulfilling computing needs, such as deep learning applied to object classification, close to where data is originated and using computing resources that include consumer electronic devices such as smartphones. Simulation tools like DewSim aid the study of resource allocation mechanisms for exploiting clusters of smartphones, however, there is a gap w.r.t software tools that allow to perform similar studies over real Dew computing testbeds. We have developed LiveDewStream, an open source project to model executable tasks derived from data streams to be run on real smartphone clusters. The project offers a key functionality missing in other tools: reproducibility of battery-driven Dew experiments. Our major contribution is to provide the community a common in vivo platform to study best-performing allocation mechanisms under different stream processing scenarios and/or deep learning inference models.Fil: Mateos Diaz, Cristian Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Hirsch, Mailén. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Toloza, Juan Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Zunino Suarez, Alejandro Octavio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaElsevier2022-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/211275Mateos Diaz, Cristian Maximiliano; Hirsch, Mailén; Toloza, Juan Manuel; Zunino Suarez, Alejandro Octavio; LiveDewStream: A stream processing platform for running in-lab distributed deep learning inferences on smartphone clusters at the edge; Elsevier; SoftwareX; 20; 12-2022; 1-62352-7110CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S2352711022001868info:eu-repo/semantics/altIdentifier/doi/10.1016/j.softx.2022.101268info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:57:50Zoai:ri.conicet.gov.ar:11336/211275instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 09:57:50.847CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv LiveDewStream: A stream processing platform for running in-lab distributed deep learning inferences on smartphone clusters at the edge
title LiveDewStream: A stream processing platform for running in-lab distributed deep learning inferences on smartphone clusters at the edge
spellingShingle LiveDewStream: A stream processing platform for running in-lab distributed deep learning inferences on smartphone clusters at the edge
Mateos Diaz, Cristian Maximiliano
MOBILE DEVICES
STREAM PROCESSING
DEEP LEARNING
DEW COMPUTING
ANDROID
title_short LiveDewStream: A stream processing platform for running in-lab distributed deep learning inferences on smartphone clusters at the edge
title_full LiveDewStream: A stream processing platform for running in-lab distributed deep learning inferences on smartphone clusters at the edge
title_fullStr LiveDewStream: A stream processing platform for running in-lab distributed deep learning inferences on smartphone clusters at the edge
title_full_unstemmed LiveDewStream: A stream processing platform for running in-lab distributed deep learning inferences on smartphone clusters at the edge
title_sort LiveDewStream: A stream processing platform for running in-lab distributed deep learning inferences on smartphone clusters at the edge
dc.creator.none.fl_str_mv Mateos Diaz, Cristian Maximiliano
Hirsch, Mailén
Toloza, Juan Manuel
Zunino Suarez, Alejandro Octavio
author Mateos Diaz, Cristian Maximiliano
author_facet Mateos Diaz, Cristian Maximiliano
Hirsch, Mailén
Toloza, Juan Manuel
Zunino Suarez, Alejandro Octavio
author_role author
author2 Hirsch, Mailén
Toloza, Juan Manuel
Zunino Suarez, Alejandro Octavio
author2_role author
author
author
dc.subject.none.fl_str_mv MOBILE DEVICES
STREAM PROCESSING
DEEP LEARNING
DEW COMPUTING
ANDROID
topic MOBILE DEVICES
STREAM PROCESSING
DEEP LEARNING
DEW COMPUTING
ANDROID
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Dew computing, an evolution of Fog computing, aims at fulfilling computing needs, such as deep learning applied to object classification, close to where data is originated and using computing resources that include consumer electronic devices such as smartphones. Simulation tools like DewSim aid the study of resource allocation mechanisms for exploiting clusters of smartphones, however, there is a gap w.r.t software tools that allow to perform similar studies over real Dew computing testbeds. We have developed LiveDewStream, an open source project to model executable tasks derived from data streams to be run on real smartphone clusters. The project offers a key functionality missing in other tools: reproducibility of battery-driven Dew experiments. Our major contribution is to provide the community a common in vivo platform to study best-performing allocation mechanisms under different stream processing scenarios and/or deep learning inference models.
Fil: Mateos Diaz, Cristian Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
Fil: Hirsch, Mailén. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
Fil: Toloza, Juan Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
Fil: Zunino Suarez, Alejandro Octavio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
description Dew computing, an evolution of Fog computing, aims at fulfilling computing needs, such as deep learning applied to object classification, close to where data is originated and using computing resources that include consumer electronic devices such as smartphones. Simulation tools like DewSim aid the study of resource allocation mechanisms for exploiting clusters of smartphones, however, there is a gap w.r.t software tools that allow to perform similar studies over real Dew computing testbeds. We have developed LiveDewStream, an open source project to model executable tasks derived from data streams to be run on real smartphone clusters. The project offers a key functionality missing in other tools: reproducibility of battery-driven Dew experiments. Our major contribution is to provide the community a common in vivo platform to study best-performing allocation mechanisms under different stream processing scenarios and/or deep learning inference models.
publishDate 2022
dc.date.none.fl_str_mv 2022-12
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/211275
Mateos Diaz, Cristian Maximiliano; Hirsch, Mailén; Toloza, Juan Manuel; Zunino Suarez, Alejandro Octavio; LiveDewStream: A stream processing platform for running in-lab distributed deep learning inferences on smartphone clusters at the edge; Elsevier; SoftwareX; 20; 12-2022; 1-6
2352-7110
CONICET Digital
CONICET
url http://hdl.handle.net/11336/211275
identifier_str_mv Mateos Diaz, Cristian Maximiliano; Hirsch, Mailén; Toloza, Juan Manuel; Zunino Suarez, Alejandro Octavio; LiveDewStream: A stream processing platform for running in-lab distributed deep learning inferences on smartphone clusters at the edge; Elsevier; SoftwareX; 20; 12-2022; 1-6
2352-7110
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S2352711022001868
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.softx.2022.101268
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.13397

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