Collaborative, distributed and scalable platform based on mobile, cloud, micro services and containers for intensive computing tasks

Autores
Petrocelli, David; De Giusti, Armando Eduardo; Naiouf, Marcelo
Año de publicación
2020
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Compute-heavy workloads are traditionally run on x86-based HPC platforms and Intel, AMD or Nvidia GPUs; these require a high initial capital expense and ongoing maintenance costs. ARM-based mobile devicesoffer a rad-ically different paradigm with substantially lower capital and maintenance costs and higher gains in performance and efficiency in recent years. When compared to their x-86 brethren, they have become ubiquitous in consumer markets and are making steady gains in the server market. Given this shifting computer paradigm, it is conceivable that a cost- and power-efficient solution for our world’s data processing would include those very same ARM-based mobile devices while they are idling. Given that context, we developed and deployed an auto-scalable, distributed and redundant platform on the basis of a cloud-based service managed via container orchestration and microservices that are in charge of recycling and optimizing these idle resources. We tested the platform performing distributed video compression. We concluded the system allows for improvements in terms of scalability, flexibility, stability, efficiency, and cost for compute-heavy work-loads.
Instituto de Investigación en Informática
Instituto de Investigación en Informática
Materia
Ciencias Informáticas
Kubernetes & Containers
Pipelines
Microservices
Cloud Computing & Storage
Mobile Computing
Distributed & Collaborative Computing
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/104766
Acceder
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oai_identifier_str oai:sedici.unlp.edu.ar:10915/104766
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Collaborative, distributed and scalable platform based on mobile, cloud, micro services and containers for intensive computing tasksPetrocelli, DavidDe Giusti, Armando EduardoNaiouf, MarceloCiencias InformáticasKubernetes & ContainersPipelinesMicroservicesCloud Computing & StorageMobile ComputingDistributed & Collaborative ComputingCompute-heavy workloads are traditionally run on x86-based HPC platforms and Intel, AMD or Nvidia GPUs; these require a high initial capital expense and ongoing maintenance costs. ARM-based mobile devicesoffer a rad-ically different paradigm with substantially lower capital and maintenance costs and higher gains in performance and efficiency in recent years. When compared to their x-86 brethren, they have become ubiquitous in consumer markets and are making steady gains in the server market. Given this shifting computer paradigm, it is conceivable that a cost- and power-efficient solution for our world’s data processing would include those very same ARM-based mobile devices while they are idling. Given that context, we developed and deployed an auto-scalable, distributed and redundant platform on the basis of a cloud-based service managed via container orchestration and microservices that are in charge of recycling and optimizing these idle resources. We tested the platform performing distributed video compression. We concluded the system allows for improvements in terms of scalability, flexibility, stability, efficiency, and cost for compute-heavy work-loads.Instituto de Investigación en InformáticaInstituto de Investigación en Informática2020-09info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf10-13http://sedici.unlp.edu.ar/handle/10915/104766enginfo:eu-repo/semantics/altIdentifier/isbn/978-950-34-1927-4info:eu-repo/semantics/reference/hdl/10915/103585info:eu-repo/semantics/reference/hdl/10915/103585info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:22:53Zoai:sedici.unlp.edu.ar:10915/104766Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:22:54.257SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Collaborative, distributed and scalable platform based on mobile, cloud, micro services and containers for intensive computing tasks
title Collaborative, distributed and scalable platform based on mobile, cloud, micro services and containers for intensive computing tasks
spellingShingle Collaborative, distributed and scalable platform based on mobile, cloud, micro services and containers for intensive computing tasks
Petrocelli, David
Ciencias Informáticas
Kubernetes & Containers
Pipelines
Microservices
Cloud Computing & Storage
Mobile Computing
Distributed & Collaborative Computing
title_short Collaborative, distributed and scalable platform based on mobile, cloud, micro services and containers for intensive computing tasks
title_full Collaborative, distributed and scalable platform based on mobile, cloud, micro services and containers for intensive computing tasks
title_fullStr Collaborative, distributed and scalable platform based on mobile, cloud, micro services and containers for intensive computing tasks
title_full_unstemmed Collaborative, distributed and scalable platform based on mobile, cloud, micro services and containers for intensive computing tasks
title_sort Collaborative, distributed and scalable platform based on mobile, cloud, micro services and containers for intensive computing tasks
dc.creator.none.fl_str_mv Petrocelli, David
De Giusti, Armando Eduardo
Naiouf, Marcelo
author Petrocelli, David
author_facet Petrocelli, David
De Giusti, Armando Eduardo
Naiouf, Marcelo
author_role author
author2 De Giusti, Armando Eduardo
Naiouf, Marcelo
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Kubernetes & Containers
Pipelines
Microservices
Cloud Computing & Storage
Mobile Computing
Distributed & Collaborative Computing
topic Ciencias Informáticas
Kubernetes & Containers
Pipelines
Microservices
Cloud Computing & Storage
Mobile Computing
Distributed & Collaborative Computing
dc.description.none.fl_txt_mv Compute-heavy workloads are traditionally run on x86-based HPC platforms and Intel, AMD or Nvidia GPUs; these require a high initial capital expense and ongoing maintenance costs. ARM-based mobile devicesoffer a rad-ically different paradigm with substantially lower capital and maintenance costs and higher gains in performance and efficiency in recent years. When compared to their x-86 brethren, they have become ubiquitous in consumer markets and are making steady gains in the server market. Given this shifting computer paradigm, it is conceivable that a cost- and power-efficient solution for our world’s data processing would include those very same ARM-based mobile devices while they are idling. Given that context, we developed and deployed an auto-scalable, distributed and redundant platform on the basis of a cloud-based service managed via container orchestration and microservices that are in charge of recycling and optimizing these idle resources. We tested the platform performing distributed video compression. We concluded the system allows for improvements in terms of scalability, flexibility, stability, efficiency, and cost for compute-heavy work-loads.
Instituto de Investigación en Informática
Instituto de Investigación en Informática
description Compute-heavy workloads are traditionally run on x86-based HPC platforms and Intel, AMD or Nvidia GPUs; these require a high initial capital expense and ongoing maintenance costs. ARM-based mobile devicesoffer a rad-ically different paradigm with substantially lower capital and maintenance costs and higher gains in performance and efficiency in recent years. When compared to their x-86 brethren, they have become ubiquitous in consumer markets and are making steady gains in the server market. Given this shifting computer paradigm, it is conceivable that a cost- and power-efficient solution for our world’s data processing would include those very same ARM-based mobile devices while they are idling. Given that context, we developed and deployed an auto-scalable, distributed and redundant platform on the basis of a cloud-based service managed via container orchestration and microservices that are in charge of recycling and optimizing these idle resources. We tested the platform performing distributed video compression. We concluded the system allows for improvements in terms of scalability, flexibility, stability, efficiency, and cost for compute-heavy work-loads.
publishDate 2020
dc.date.none.fl_str_mv 2020-09
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/104766
url http://sedici.unlp.edu.ar/handle/10915/104766
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/isbn/978-950-34-1927-4
info:eu-repo/semantics/reference/hdl/10915/103585
info:eu-repo/semantics/reference/hdl/10915/103585
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
10-13
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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score 13.069144

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