Rescheduling serverless workloads across the cloud-to-edge continuum

Serverless computing was a breakthrough in Cloud computing due to its high elasticity capabilities and fine-grained pay-per-use model offered by the main public Cloud providers. Meanwhile, open-source serverless platforms supporting the FaaS (Function as a Service) model allow users to take advantag...

ver descrição completa

Detalhes bibliográficos
Autores: Risco, Sebastián, Alarcón, Caterina, Langarita, Sergio, Caballer, Miguel, Moltó, Germán
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2024
País:España
Recursos:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/389658
Acesso em linha:http://hdl.handle.net/10261/389658
https://api.elsevier.com/content/abstract/scopus_id/85180528330
Access Level:acceso abierto
Palavra-chave:Cloud computing
Cloud-to-edge continuum
Containers
FaaS
Kubernetes
Serverless computing
id ES_c25843baa98bbc82ea9f04b016cba384
oai_identifier_str oai:digital.csic.es:10261/389658
network_acronym_str ES
network_name_str España
repository_id_str
spelling Rescheduling serverless workloads across the cloud-to-edge continuumRisco, SebastiánAlarcón, CaterinaLangarita, SergioCaballer, MiguelMoltó, GermánCloud computingCloud-to-edge continuumContainersFaaSKubernetesServerless computingServerless computing was a breakthrough in Cloud computing due to its high elasticity capabilities and fine-grained pay-per-use model offered by the main public Cloud providers. Meanwhile, open-source serverless platforms supporting the FaaS (Function as a Service) model allow users to take advantage of many of their benefits while operating on the on-premises platforms of organizations. This opens the possibility to deploy and exploit them on the different layers of the cloud-to-edge continuum, either on IoT (Internet of Things) devices located at the Edge (i.e. next to data acquisition devices), in on-premises clusters closer to the data sources (i.e. Fog computing) or directly on the Cloud. This paper presents two strategies to mitigate the overload that disparate data ingestion rates may cause in low-powered devices at the Edge or Fog layers. To this end, it is proposed to delegate and reschedule serverless jobs between the different layers of the cloud-to-edge continuum using an open-source platform for event-driven file processing. To demonstrate the performance of these strategies, a use case for fire detection is proposed that includes processing in the Fog via minified Kubernetes clusters located near the Edge, in the private Cloud via on-premises elastic clusters and, finally, in the public Cloud by using the AWS (Amazon Web Services) Lambda FaaS service. The results indicate that these strategies can mitigate overloads in use cases involving processing across the cloud-to-edge continuum by coordinating several layers of computing resources.Grant PID2020-113126RB-I00 funded by MCIN/AEI/10.13039/501100011033. Project PDC2021-120844-I00 funded by MCIN/AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR. This work was supported by the project AI-SPRINT “AI in Secure Privacy-Preserving Computing Continuum” that has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant 101016577. This work was also supported by the project AI4EOSC “Artificial Intelligence for the European Open Science Cloud” that has received funding from the European Union’s Horizon Europe Research and Innovation Programme under Grant 101058593.Peer reviewedElsevier BVAgencia Estatal de Investigación (España)Ministerio de Ciencia, Innovación y Universidades (España)European CommissionRisco, Sebastián [0000-0002-7710-2182]Moltó, Germán [0000-0002-8049-253X]Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202520252024info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10261/389658https://api.elsevier.com/content/abstract/scopus_id/85180528330reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Español#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-113126RB-I00info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PDC2021-120844-I00info:eu-repo/grantAgreement/EC/H2020/101016577info:eu-repo/grantAgreement/EC/HE/101058593The underlying dataset has been published as supplementary material of the article in the publisher platform at DOI https://doi.org/10.1016/j.future.2023.12.015https://doi.org/10.1016/j.future.2023.12.015Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3896582026-05-22T06:33:51Z
dc.title.none.fl_str_mv Rescheduling serverless workloads across the cloud-to-edge continuum
title Rescheduling serverless workloads across the cloud-to-edge continuum
spellingShingle Rescheduling serverless workloads across the cloud-to-edge continuum
Risco, Sebastián
Cloud computing
Cloud-to-edge continuum
Containers
FaaS
Kubernetes
Serverless computing
title_short Rescheduling serverless workloads across the cloud-to-edge continuum
title_full Rescheduling serverless workloads across the cloud-to-edge continuum
title_fullStr Rescheduling serverless workloads across the cloud-to-edge continuum
title_full_unstemmed Rescheduling serverless workloads across the cloud-to-edge continuum
title_sort Rescheduling serverless workloads across the cloud-to-edge continuum
dc.creator.none.fl_str_mv Risco, Sebastián
Alarcón, Caterina
Langarita, Sergio
Caballer, Miguel
Moltó, Germán
author Risco, Sebastián
author_facet Risco, Sebastián
Alarcón, Caterina
Langarita, Sergio
Caballer, Miguel
Moltó, Germán
author_role author
author2 Alarcón, Caterina
Langarita, Sergio
Caballer, Miguel
Moltó, Germán
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Agencia Estatal de Investigación (España)
Ministerio de Ciencia, Innovación y Universidades (España)
European Commission
Risco, Sebastián [0000-0002-7710-2182]
Moltó, Germán [0000-0002-8049-253X]
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Cloud computing
Cloud-to-edge continuum
Containers
FaaS
Kubernetes
Serverless computing
topic Cloud computing
Cloud-to-edge continuum
Containers
FaaS
Kubernetes
Serverless computing
description Serverless computing was a breakthrough in Cloud computing due to its high elasticity capabilities and fine-grained pay-per-use model offered by the main public Cloud providers. Meanwhile, open-source serverless platforms supporting the FaaS (Function as a Service) model allow users to take advantage of many of their benefits while operating on the on-premises platforms of organizations. This opens the possibility to deploy and exploit them on the different layers of the cloud-to-edge continuum, either on IoT (Internet of Things) devices located at the Edge (i.e. next to data acquisition devices), in on-premises clusters closer to the data sources (i.e. Fog computing) or directly on the Cloud. This paper presents two strategies to mitigate the overload that disparate data ingestion rates may cause in low-powered devices at the Edge or Fog layers. To this end, it is proposed to delegate and reschedule serverless jobs between the different layers of the cloud-to-edge continuum using an open-source platform for event-driven file processing. To demonstrate the performance of these strategies, a use case for fire detection is proposed that includes processing in the Fog via minified Kubernetes clusters located near the Edge, in the private Cloud via on-premises elastic clusters and, finally, in the public Cloud by using the AWS (Amazon Web Services) Lambda FaaS service. The results indicate that these strategies can mitigate overloads in use cases involving processing across the cloud-to-edge continuum by coordinating several layers of computing resources.
publishDate 2024
dc.date.none.fl_str_mv 2024
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/389658
https://api.elsevier.com/content/abstract/scopus_id/85180528330
url http://hdl.handle.net/10261/389658
https://api.elsevier.com/content/abstract/scopus_id/85180528330
dc.language.none.fl_str_mv Español
language_invalid_str_mv Español
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-113126RB-I00
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PDC2021-120844-I00
info:eu-repo/grantAgreement/EC/H2020/101016577
info:eu-repo/grantAgreement/EC/HE/101058593
The underlying dataset has been published as supplementary material of the article in the publisher platform at DOI https://doi.org/10.1016/j.future.2023.12.015
https://doi.org/10.1016/j.future.2023.12.015
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier BV
publisher.none.fl_str_mv Elsevier BV
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869418658099363840
score 15,198674

Registos relacionados