BAGESS: A Software Module Based on a Genetic Algorithm to Sequentially Order Load-Balancing Evaluation Scenarios Over Smartphone-Based Clusters at the Edge

Autores
Yannibelli, Virginia Daniela; Hirsch Jofré, Matías Eberardo; Toloza, Juan Manuel; Majchrzak, Tim A.; Grønli, Tor Morten; Zunino Suarez, Alejandro Octavio; Mateos Diaz, Cristian Maximiliano
Año de publicación
2024
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Due to the increasing interest in employing smartphones as first-class citizens in high-performance Edge computing environments, the necessity of software to facilitate the evaluation of load-balancing strategies for smartphone-based clusters has emerged. Regarding this, to select the best strategy for a cluster with m smartphones, usually a number of g candidate strategies are evaluated based on a number of r scenarios that contain these smartphones, which differ in terms of the start battery levels required for these smartphones. Thus, each of the r scenarios must be prepared before evaluating each of the g strategies on each ri , so that the smartphones have the required start battery levels pre-configured for ri , which requires discharging or charging smartphones. This leads to a number of e = r*g scenario preparation events that must be sequentially developed, considering that the time required to develop each event depends on the previous event. Thus, the single-objective problem addressed here implies finding out the sequential order in which the events should be developed, so that the total time required to develop them is minimized. This problem is modeled as the ATSP (Asymmetric Traveling Salesman Problem), since defining the sequential order to develop the events is equivalent to defining the sequential order to visit the cities, and therefore, is an NP-Hard problem. Given the complexity of this problem, the novel software module BAGESS (Battery Aware Green Edge Scenario Sequencer) is proposed, which uses a genetic algorithm for defining the sequential order to develop the events. BAGESS’s performance outperforms those of the methods currently used for the problem, reaching significant savings regarding the time required to develop the events in the range [12, 85]%.
Fil: Yannibelli, Virginia Daniela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
Fil: Hirsch Jofré, Matías Eberardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
Fil: Toloza, Juan Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
Fil: Majchrzak, Tim A.. University Of Agder; Noruega
Fil: Grønli, Tor Morten. Kristiania University College; Noruega
Fil: Zunino Suarez, Alejandro Octavio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
Fil: Mateos Diaz, Cristian Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
Materia
EDGE COMPUTING
SMARTPHONE
PROFILING
BENCHMARKING
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/260493
Acceder
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repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling BAGESS: A Software Module Based on a Genetic Algorithm to Sequentially Order Load-Balancing Evaluation Scenarios Over Smartphone-Based Clusters at the EdgeYannibelli, Virginia DanielaHirsch Jofré, Matías EberardoToloza, Juan ManuelMajchrzak, Tim A.Grønli, Tor MortenZunino Suarez, Alejandro OctavioMateos Diaz, Cristian MaximilianoEDGE COMPUTINGSMARTPHONEPROFILINGBENCHMARKINGhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Due to the increasing interest in employing smartphones as first-class citizens in high-performance Edge computing environments, the necessity of software to facilitate the evaluation of load-balancing strategies for smartphone-based clusters has emerged. Regarding this, to select the best strategy for a cluster with m smartphones, usually a number of g candidate strategies are evaluated based on a number of r scenarios that contain these smartphones, which differ in terms of the start battery levels required for these smartphones. Thus, each of the r scenarios must be prepared before evaluating each of the g strategies on each ri , so that the smartphones have the required start battery levels pre-configured for ri , which requires discharging or charging smartphones. This leads to a number of e = r*g scenario preparation events that must be sequentially developed, considering that the time required to develop each event depends on the previous event. Thus, the single-objective problem addressed here implies finding out the sequential order in which the events should be developed, so that the total time required to develop them is minimized. This problem is modeled as the ATSP (Asymmetric Traveling Salesman Problem), since defining the sequential order to develop the events is equivalent to defining the sequential order to visit the cities, and therefore, is an NP-Hard problem. Given the complexity of this problem, the novel software module BAGESS (Battery Aware Green Edge Scenario Sequencer) is proposed, which uses a genetic algorithm for defining the sequential order to develop the events. BAGESS’s performance outperforms those of the methods currently used for the problem, reaching significant savings regarding the time required to develop the events in the range [12, 85]%.Fil: Yannibelli, Virginia Daniela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Hirsch Jofré, Matías Eberardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Toloza, Juan Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Majchrzak, Tim A.. University Of Agder; NoruegaFil: Grønli, Tor Morten. Kristiania University College; NoruegaFil: Zunino Suarez, Alejandro Octavio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Mateos Diaz, Cristian Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaInstitute of Electrical and Electronics Engineers2024-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/260493Yannibelli, Virginia Daniela; Hirsch Jofré, Matías Eberardo; Toloza, Juan Manuel; Majchrzak, Tim A.; Grønli, Tor Morten; et al.; BAGESS: A Software Module Based on a Genetic Algorithm to Sequentially Order Load-Balancing Evaluation Scenarios Over Smartphone-Based Clusters at the Edge; Institute of Electrical and Electronics Engineers; IEEE Access; 12; 9-2024; 145893-1459192169-3536CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://ieeexplore.ieee.org/document/10697165/info:eu-repo/semantics/altIdentifier/doi/10.1109/ACCESS.2024.3469641info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T14:59:04Zoai:ri.conicet.gov.ar:11336/260493instacron: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:34982025-10-15 14:59:04.881CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv BAGESS: A Software Module Based on a Genetic Algorithm to Sequentially Order Load-Balancing Evaluation Scenarios Over Smartphone-Based Clusters at the Edge
title BAGESS: A Software Module Based on a Genetic Algorithm to Sequentially Order Load-Balancing Evaluation Scenarios Over Smartphone-Based Clusters at the Edge
spellingShingle BAGESS: A Software Module Based on a Genetic Algorithm to Sequentially Order Load-Balancing Evaluation Scenarios Over Smartphone-Based Clusters at the Edge
Yannibelli, Virginia Daniela
EDGE COMPUTING
SMARTPHONE
PROFILING
BENCHMARKING
title_short BAGESS: A Software Module Based on a Genetic Algorithm to Sequentially Order Load-Balancing Evaluation Scenarios Over Smartphone-Based Clusters at the Edge
title_full BAGESS: A Software Module Based on a Genetic Algorithm to Sequentially Order Load-Balancing Evaluation Scenarios Over Smartphone-Based Clusters at the Edge
title_fullStr BAGESS: A Software Module Based on a Genetic Algorithm to Sequentially Order Load-Balancing Evaluation Scenarios Over Smartphone-Based Clusters at the Edge
title_full_unstemmed BAGESS: A Software Module Based on a Genetic Algorithm to Sequentially Order Load-Balancing Evaluation Scenarios Over Smartphone-Based Clusters at the Edge
title_sort BAGESS: A Software Module Based on a Genetic Algorithm to Sequentially Order Load-Balancing Evaluation Scenarios Over Smartphone-Based Clusters at the Edge
dc.creator.none.fl_str_mv Yannibelli, Virginia Daniela
Hirsch Jofré, Matías Eberardo
Toloza, Juan Manuel
Majchrzak, Tim A.
Grønli, Tor Morten
Zunino Suarez, Alejandro Octavio
Mateos Diaz, Cristian Maximiliano
author Yannibelli, Virginia Daniela
author_facet Yannibelli, Virginia Daniela
Hirsch Jofré, Matías Eberardo
Toloza, Juan Manuel
Majchrzak, Tim A.
Grønli, Tor Morten
Zunino Suarez, Alejandro Octavio
Mateos Diaz, Cristian Maximiliano
author_role author
author2 Hirsch Jofré, Matías Eberardo
Toloza, Juan Manuel
Majchrzak, Tim A.
Grønli, Tor Morten
Zunino Suarez, Alejandro Octavio
Mateos Diaz, Cristian Maximiliano
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv EDGE COMPUTING
SMARTPHONE
PROFILING
BENCHMARKING
topic EDGE COMPUTING
SMARTPHONE
PROFILING
BENCHMARKING
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Due to the increasing interest in employing smartphones as first-class citizens in high-performance Edge computing environments, the necessity of software to facilitate the evaluation of load-balancing strategies for smartphone-based clusters has emerged. Regarding this, to select the best strategy for a cluster with m smartphones, usually a number of g candidate strategies are evaluated based on a number of r scenarios that contain these smartphones, which differ in terms of the start battery levels required for these smartphones. Thus, each of the r scenarios must be prepared before evaluating each of the g strategies on each ri , so that the smartphones have the required start battery levels pre-configured for ri , which requires discharging or charging smartphones. This leads to a number of e = r*g scenario preparation events that must be sequentially developed, considering that the time required to develop each event depends on the previous event. Thus, the single-objective problem addressed here implies finding out the sequential order in which the events should be developed, so that the total time required to develop them is minimized. This problem is modeled as the ATSP (Asymmetric Traveling Salesman Problem), since defining the sequential order to develop the events is equivalent to defining the sequential order to visit the cities, and therefore, is an NP-Hard problem. Given the complexity of this problem, the novel software module BAGESS (Battery Aware Green Edge Scenario Sequencer) is proposed, which uses a genetic algorithm for defining the sequential order to develop the events. BAGESS’s performance outperforms those of the methods currently used for the problem, reaching significant savings regarding the time required to develop the events in the range [12, 85]%.
Fil: Yannibelli, Virginia Daniela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
Fil: Hirsch Jofré, Matías Eberardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
Fil: Toloza, Juan Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
Fil: Majchrzak, Tim A.. University Of Agder; Noruega
Fil: Grønli, Tor Morten. Kristiania University College; Noruega
Fil: Zunino Suarez, Alejandro Octavio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
Fil: Mateos Diaz, Cristian Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
description Due to the increasing interest in employing smartphones as first-class citizens in high-performance Edge computing environments, the necessity of software to facilitate the evaluation of load-balancing strategies for smartphone-based clusters has emerged. Regarding this, to select the best strategy for a cluster with m smartphones, usually a number of g candidate strategies are evaluated based on a number of r scenarios that contain these smartphones, which differ in terms of the start battery levels required for these smartphones. Thus, each of the r scenarios must be prepared before evaluating each of the g strategies on each ri , so that the smartphones have the required start battery levels pre-configured for ri , which requires discharging or charging smartphones. This leads to a number of e = r*g scenario preparation events that must be sequentially developed, considering that the time required to develop each event depends on the previous event. Thus, the single-objective problem addressed here implies finding out the sequential order in which the events should be developed, so that the total time required to develop them is minimized. This problem is modeled as the ATSP (Asymmetric Traveling Salesman Problem), since defining the sequential order to develop the events is equivalent to defining the sequential order to visit the cities, and therefore, is an NP-Hard problem. Given the complexity of this problem, the novel software module BAGESS (Battery Aware Green Edge Scenario Sequencer) is proposed, which uses a genetic algorithm for defining the sequential order to develop the events. BAGESS’s performance outperforms those of the methods currently used for the problem, reaching significant savings regarding the time required to develop the events in the range [12, 85]%.
publishDate 2024
dc.date.none.fl_str_mv 2024-09
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/11336/260493
Yannibelli, Virginia Daniela; Hirsch Jofré, Matías Eberardo; Toloza, Juan Manuel; Majchrzak, Tim A.; Grønli, Tor Morten; et al.; BAGESS: A Software Module Based on a Genetic Algorithm to Sequentially Order Load-Balancing Evaluation Scenarios Over Smartphone-Based Clusters at the Edge; Institute of Electrical and Electronics Engineers; IEEE Access; 12; 9-2024; 145893-145919
2169-3536
CONICET Digital
CONICET
url http://hdl.handle.net/11336/260493
identifier_str_mv Yannibelli, Virginia Daniela; Hirsch Jofré, Matías Eberardo; Toloza, Juan Manuel; Majchrzak, Tim A.; Grønli, Tor Morten; et al.; BAGESS: A Software Module Based on a Genetic Algorithm to Sequentially Order Load-Balancing Evaluation Scenarios Over Smartphone-Based Clusters at the Edge; Institute of Electrical and Electronics Engineers; IEEE Access; 12; 9-2024; 145893-145919
2169-3536
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://ieeexplore.ieee.org/document/10697165/
info:eu-repo/semantics/altIdentifier/doi/10.1109/ACCESS.2024.3469641
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
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 13.22299

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