Performance characterization of video analytics workloads in heterogeneous edge infrastructures

Powered by deep learning, video analytic applications process millions of camera feeds in real-time to extract meaningful information from their surroundings. And this number grows by the minute. To avoid saturating the backhaul network and provide lower latencies, a distributed and heterogeneous ed...

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Autores: Rivas Barragan, Daniel, Guim Bernat, Francesc, Polo Bardés, Jorda, Carrera Pérez, David|||0000-0003-4898-3424
Tipo de recurso: artículo
Fecha de publicación:2021
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/346081
Acceso en línea:https://hdl.handle.net/2117/346081
https://dx.doi.org/10.1002/cpe.6317
Access Level:acceso abierto
Palabra clave:Cloud computing
Computer vision
Optical data processing
DNN
Edge cloud
End-to-end video analytics
Inference
Video analytics
Video decoding
Computació en núvol
Visió per ordinador
Processament òptic de dades
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo
Àrees temàtiques de la UPC::Informàtica::Arquitectura de computadors
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oai_identifier_str oai:upcommons.upc.edu:2117/346081
network_acronym_str ES
network_name_str España
repository_id_str
spelling Performance characterization of video analytics workloads in heterogeneous edge infrastructuresRivas Barragan, DanielGuim Bernat, FrancescPolo Bardés, JordaCarrera Pérez, David|||0000-0003-4898-3424Cloud computingComputer visionOptical data processingDNNEdge cloudEnd-to-end video analyticsInferenceVideo analyticsVideo decodingComputació en núvolVisió per ordinadorProcessament òptic de dadesÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeoÀrees temàtiques de la UPC::Informàtica::Arquitectura de computadorsPowered by deep learning, video analytic applications process millions of camera feeds in real-time to extract meaningful information from their surroundings. And this number grows by the minute. To avoid saturating the backhaul network and provide lower latencies, a distributed and heterogeneous edge cloud is postulated as a key enabler for widespread video analytics. This article provides a complete characterization of end-to-end video analytics across a set of hardware platforms and different neural network architectures. Each platform is selected to fill a different gap in a distributed, shared, and heterogeneous infrastructure. Moreover, we analyze how performance scales on each of these platforms with respect to the amount of resources dedicated to video analytics. Finally, we extract the key conclusions of the characterization to build an experimental model to estimate performance and cost of end-to-end video analytics in different edge scenarios. Our experiments show that managing video analytics workloads efficiently requires awareness of both, the platforms in which these are executed, and the full end-to-end pipeline. To the best of our knowledge, this is the first work that provides a complete characterization of end-to-end video analytics in heterogeneous edge platforms.This work was supported by the Ministry of Economy of Spain under contract TIN2015-65316-P and the Ministry of Science under contract PID2019-107255GB-C21/AEI/10.13039/501100011033; the Generalitat de Catalunya under contract 2014SGR1051; the ICREA Academia program; and Intel CorporationPeer ReviewedWiley (John Wiley & Sons)20232023-06-2520212021-05-26journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/346081https://dx.doi.org/10.1002/cpe.6317reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)InglésengMinisterio de Economía y Competitividad http://doi.org/10.13039/501100003329 TIN2015-65316-P COMPUTACION DE ALTAS PRESTACIONES VIIopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial 4.0 Internationalhttps://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/3460812026-05-27T15:37:01Z
dc.title.none.fl_str_mv Performance characterization of video analytics workloads in heterogeneous edge infrastructures
title Performance characterization of video analytics workloads in heterogeneous edge infrastructures
spellingShingle Performance characterization of video analytics workloads in heterogeneous edge infrastructures
Rivas Barragan, Daniel
Cloud computing
Computer vision
Optical data processing
DNN
Edge cloud
End-to-end video analytics
Inference
Video analytics
Video decoding
Computació en núvol
Visió per ordinador
Processament òptic de dades
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo
Àrees temàtiques de la UPC::Informàtica::Arquitectura de computadors
title_short Performance characterization of video analytics workloads in heterogeneous edge infrastructures
title_full Performance characterization of video analytics workloads in heterogeneous edge infrastructures
title_fullStr Performance characterization of video analytics workloads in heterogeneous edge infrastructures
title_full_unstemmed Performance characterization of video analytics workloads in heterogeneous edge infrastructures
title_sort Performance characterization of video analytics workloads in heterogeneous edge infrastructures
dc.creator.none.fl_str_mv Rivas Barragan, Daniel
Guim Bernat, Francesc
Polo Bardés, Jorda
Carrera Pérez, David|||0000-0003-4898-3424
author Rivas Barragan, Daniel
author_facet Rivas Barragan, Daniel
Guim Bernat, Francesc
Polo Bardés, Jorda
Carrera Pérez, David|||0000-0003-4898-3424
author_role author
author2 Guim Bernat, Francesc
Polo Bardés, Jorda
Carrera Pérez, David|||0000-0003-4898-3424
author2_role author
author
author
dc.subject.none.fl_str_mv Cloud computing
Computer vision
Optical data processing
DNN
Edge cloud
End-to-end video analytics
Inference
Video analytics
Video decoding
Computació en núvol
Visió per ordinador
Processament òptic de dades
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo
Àrees temàtiques de la UPC::Informàtica::Arquitectura de computadors
topic Cloud computing
Computer vision
Optical data processing
DNN
Edge cloud
End-to-end video analytics
Inference
Video analytics
Video decoding
Computació en núvol
Visió per ordinador
Processament òptic de dades
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo
Àrees temàtiques de la UPC::Informàtica::Arquitectura de computadors
description Powered by deep learning, video analytic applications process millions of camera feeds in real-time to extract meaningful information from their surroundings. And this number grows by the minute. To avoid saturating the backhaul network and provide lower latencies, a distributed and heterogeneous edge cloud is postulated as a key enabler for widespread video analytics. This article provides a complete characterization of end-to-end video analytics across a set of hardware platforms and different neural network architectures. Each platform is selected to fill a different gap in a distributed, shared, and heterogeneous infrastructure. Moreover, we analyze how performance scales on each of these platforms with respect to the amount of resources dedicated to video analytics. Finally, we extract the key conclusions of the characterization to build an experimental model to estimate performance and cost of end-to-end video analytics in different edge scenarios. Our experiments show that managing video analytics workloads efficiently requires awareness of both, the platforms in which these are executed, and the full end-to-end pipeline. To the best of our knowledge, this is the first work that provides a complete characterization of end-to-end video analytics in heterogeneous edge platforms.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021-05-26
2023
2023-06-25
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/346081
https://dx.doi.org/10.1002/cpe.6317
url https://hdl.handle.net/2117/346081
https://dx.doi.org/10.1002/cpe.6317
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Ministerio de Economía y Competitividad http://doi.org/10.13039/501100003329 TIN2015-65316-P COMPUTACION DE ALTAS PRESTACIONES VII
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial 4.0 International
https://creativecommons.org/licenses/by-nc/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial 4.0 International
https://creativecommons.org/licenses/by-nc/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Wiley (John Wiley & Sons)
publisher.none.fl_str_mv Wiley (John Wiley & Sons)
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869418503045382144
score 15,300719

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