Autoscaling scientific workflows on the cloud by combining on-demand and spot instances

Autores
Monge Bosdari, David Antonio; Garí Núñez, Yisel; Mateos Diaz, Cristian Maximiliano; Garcia Garino, Carlos Gabriel
Año de publicación
2017
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Autoscaling strategies achieve efficient and cheap executions of scientific workflows running in the cloud by determining appropriate type and amount of virtual machine instances to use while scheduling the tasks/data . Current strategies only consider on-demand instances ignoring the advantages of a mixed cloud infrastructure comprising also spot instances. Although the latter type of instances are subject to failures and therefore provide an unreliable infrastructure, they potentially offer significant cost and time improvements if used wisely. This paper discusses a novel autoscaling strategy with two features. First, it combines both types of instances to acquire a better cost-performance balance in the infrastructure. And second, it uses heuristic scheduling to deal with the unreliability of spot instances. Simulated experiments based on 4 scientific workflows showed substantial makespan and cost reductions of our strategy when compared with a reference strategy from the state of the art entitled Scaling First. These promising results represent a step towards new and better strategies for workflow autoscaling in the cloud.
Fil: Monge Bosdari, David Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentina
Fil: Garí Núñez, Yisel. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina
Fil: Mateos Diaz, Cristian Maximiliano. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Instituto de Sistemas Tandil; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil; Argentina
Fil: Garcia Garino, Carlos Gabriel. Universidad Nacional de Cuyo. Facultad de Ingeniería; Argentina. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
Scientific Workflows
Cloud Computing
Autoscaling
Scheduling
Spot Instances
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/69858
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Autoscaling scientific workflows on the cloud by combining on-demand and spot instancesMonge Bosdari, David AntonioGarí Núñez, YiselMateos Diaz, Cristian MaximilianoGarcia Garino, Carlos GabrielScientific WorkflowsCloud ComputingAutoscalingSchedulingSpot Instanceshttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Autoscaling strategies achieve efficient and cheap executions of scientific workflows running in the cloud by determining appropriate type and amount of virtual machine instances to use while scheduling the tasks/data . Current strategies only consider on-demand instances ignoring the advantages of a mixed cloud infrastructure comprising also spot instances. Although the latter type of instances are subject to failures and therefore provide an unreliable infrastructure, they potentially offer significant cost and time improvements if used wisely. This paper discusses a novel autoscaling strategy with two features. First, it combines both types of instances to acquire a better cost-performance balance in the infrastructure. And second, it uses heuristic scheduling to deal with the unreliability of spot instances. Simulated experiments based on 4 scientific workflows showed substantial makespan and cost reductions of our strategy when compared with a reference strategy from the state of the art entitled Scaling First. These promising results represent a step towards new and better strategies for workflow autoscaling in the cloud.Fil: Monge Bosdari, David Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; ArgentinaFil: Garí Núñez, Yisel. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; ArgentinaFil: Mateos Diaz, Cristian Maximiliano. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Instituto de Sistemas Tandil; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil; ArgentinaFil: Garcia Garino, Carlos Gabriel. Universidad Nacional de Cuyo. Facultad de Ingeniería; Argentina. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaC R L Publishing Ltd2017-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/69858Monge Bosdari, David Antonio; Garí Núñez, Yisel; Mateos Diaz, Cristian Maximiliano; Garcia Garino, Carlos Gabriel; Autoscaling scientific workflows on the cloud by combining on-demand and spot instances; C R L Publishing Ltd; Computer Systems Science And Engineering; 32; 4; 2-2017; 1-160267-6192CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://crl-publishing.co.uk/csse-journal/info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:51:08Zoai:ri.conicet.gov.ar:11336/69858instacron: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 09:51:08.81CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Autoscaling scientific workflows on the cloud by combining on-demand and spot instances
title Autoscaling scientific workflows on the cloud by combining on-demand and spot instances
spellingShingle Autoscaling scientific workflows on the cloud by combining on-demand and spot instances
Monge Bosdari, David Antonio
Scientific Workflows
Cloud Computing
Autoscaling
Scheduling
Spot Instances
title_short Autoscaling scientific workflows on the cloud by combining on-demand and spot instances
title_full Autoscaling scientific workflows on the cloud by combining on-demand and spot instances
title_fullStr Autoscaling scientific workflows on the cloud by combining on-demand and spot instances
title_full_unstemmed Autoscaling scientific workflows on the cloud by combining on-demand and spot instances
title_sort Autoscaling scientific workflows on the cloud by combining on-demand and spot instances
dc.creator.none.fl_str_mv Monge Bosdari, David Antonio
Garí Núñez, Yisel
Mateos Diaz, Cristian Maximiliano
Garcia Garino, Carlos Gabriel
author Monge Bosdari, David Antonio
author_facet Monge Bosdari, David Antonio
Garí Núñez, Yisel
Mateos Diaz, Cristian Maximiliano
Garcia Garino, Carlos Gabriel
author_role author
author2 Garí Núñez, Yisel
Mateos Diaz, Cristian Maximiliano
Garcia Garino, Carlos Gabriel
author2_role author
author
author
dc.subject.none.fl_str_mv Scientific Workflows
Cloud Computing
Autoscaling
Scheduling
Spot Instances
topic Scientific Workflows
Cloud Computing
Autoscaling
Scheduling
Spot Instances
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Autoscaling strategies achieve efficient and cheap executions of scientific workflows running in the cloud by determining appropriate type and amount of virtual machine instances to use while scheduling the tasks/data . Current strategies only consider on-demand instances ignoring the advantages of a mixed cloud infrastructure comprising also spot instances. Although the latter type of instances are subject to failures and therefore provide an unreliable infrastructure, they potentially offer significant cost and time improvements if used wisely. This paper discusses a novel autoscaling strategy with two features. First, it combines both types of instances to acquire a better cost-performance balance in the infrastructure. And second, it uses heuristic scheduling to deal with the unreliability of spot instances. Simulated experiments based on 4 scientific workflows showed substantial makespan and cost reductions of our strategy when compared with a reference strategy from the state of the art entitled Scaling First. These promising results represent a step towards new and better strategies for workflow autoscaling in the cloud.
Fil: Monge Bosdari, David Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentina
Fil: Garí Núñez, Yisel. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina
Fil: Mateos Diaz, Cristian Maximiliano. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Instituto de Sistemas Tandil; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil; Argentina
Fil: Garcia Garino, Carlos Gabriel. Universidad Nacional de Cuyo. Facultad de Ingeniería; Argentina. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description Autoscaling strategies achieve efficient and cheap executions of scientific workflows running in the cloud by determining appropriate type and amount of virtual machine instances to use while scheduling the tasks/data . Current strategies only consider on-demand instances ignoring the advantages of a mixed cloud infrastructure comprising also spot instances. Although the latter type of instances are subject to failures and therefore provide an unreliable infrastructure, they potentially offer significant cost and time improvements if used wisely. This paper discusses a novel autoscaling strategy with two features. First, it combines both types of instances to acquire a better cost-performance balance in the infrastructure. And second, it uses heuristic scheduling to deal with the unreliability of spot instances. Simulated experiments based on 4 scientific workflows showed substantial makespan and cost reductions of our strategy when compared with a reference strategy from the state of the art entitled Scaling First. These promising results represent a step towards new and better strategies for workflow autoscaling in the cloud.
publishDate 2017
dc.date.none.fl_str_mv 2017-02
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/11336/69858
Monge Bosdari, David Antonio; Garí Núñez, Yisel; Mateos Diaz, Cristian Maximiliano; Garcia Garino, Carlos Gabriel; Autoscaling scientific workflows on the cloud by combining on-demand and spot instances; C R L Publishing Ltd; Computer Systems Science And Engineering; 32; 4; 2-2017; 1-16
0267-6192
CONICET Digital
CONICET
url http://hdl.handle.net/11336/69858
identifier_str_mv Monge Bosdari, David Antonio; Garí Núñez, Yisel; Mateos Diaz, Cristian Maximiliano; Garcia Garino, Carlos Gabriel; Autoscaling scientific workflows on the cloud by combining on-demand and spot instances; C R L Publishing Ltd; Computer Systems Science And Engineering; 32; 4; 2-2017; 1-16
0267-6192
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://crl-publishing.co.uk/csse-journal/
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv C R L Publishing Ltd
publisher.none.fl_str_mv C R L Publishing Ltd
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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