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dc.contributor.author
Pacini Naumovich, Elina Rocío
dc.contributor.author
Mateos Diaz, Cristian Maximiliano
dc.contributor.author
Garcia Garino, Carlos Gabriel
dc.date.available
2016-07-29T21:38:39Z
dc.date.issued
2015-06
dc.identifier.citation
Pacini Naumovich, Elina Rocío; Mateos Diaz, Cristian Maximiliano; Garcia Garino, Carlos Gabriel; Balancing throughput and response time in online scientific Clouds via Ant Colony Optimization (SP2013/2013/00006); Elsevier; Advances In Engineering Software; 84; 6-2015; 31-47
dc.identifier.issn
0965-9978
dc.identifier.uri
http://hdl.handle.net/11336/6829
dc.description.abstract
The Cloud Computing paradigm focuses on the provisioning of reliable and scalable infrastructures (Clouds) delivering execution and storage services. The paradigm, with its promise of virtually infinite resources, seems to suit well in solving resource greedy scientific computing problems. The goal of this work is to study private Clouds to execute scientific experiments coming from multiple users, i.e., our work focuses on the Infrastructure as a Service (IaaS) model where custom Virtual Machines (VM) are launched in appropriate hosts available in a Cloud. Then, correctly scheduling Cloud hosts is very important and it is necessary to develop efficient scheduling strategies to appropriately allocate VMs to physical resources. The job scheduling problem is however NP-complete, and therefore many heuristics have been developed. In this work, we describe and evaluate a Cloud scheduler based on Ant Colony Optimization (ACO). The main performance metrics to study are the number of serviced users by the Cloud and the total number of created VMs in online (non-batch) scheduling scenarios. Besides, the number of intra-Cloud network messages sent are evaluated. Simulated experiments performed using CloudSim and job data from real scientific problems show that our scheduler succeeds in balancing the studied metrics compared to schedulers based on Random assignment and Genetic Algorithms.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.subject
Cloud Computing
dc.subject
Scientific Problems
dc.subject
Job Scheduling
dc.subject
Swarm Intelligence
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Ant Colony Optimization
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Genetic Algorithms
dc.subject.classification
Ciencias de la Computación
dc.subject.classification
Ciencias de la Computación e Información
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
dc.title
Balancing throughput and response time in online scientific Clouds via Ant Colony Optimization (SP2013/2013/00006)
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2016-07-29T18:33:00Z
dc.journal.volume
84
dc.journal.pagination
31-47
dc.journal.pais
Países Bajos
dc.journal.ciudad
Amsterdam
dc.description.fil
Fil: Pacini Naumovich, Elina Rocío. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentina
dc.description.fil
Fil: Mateos Diaz, Cristian Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; Argentina
dc.description.fil
Fil: Garcia Garino, Carlos Gabriel. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentina. Universidad Nacional de Cuyo. Facultad de Ingenieria; Argentina
dc.journal.title
Advances In Engineering Software
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S096599781500006X
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.advengsoft.2015.01.005
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.advengsoft.2015.01.005
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