Algorithm for 5G Resource Management Optimization in Edge Computing
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://hdl.handle.net/11449/218298 |
Resumo: | The Internet of Things (IoT) brings new applications and challenges related to cloud computing. The service distribution challenge is becoming more evident and a need for better service options is emerging. The focus of the work is to optimize issues related to the allocation of resources in Edge Computing, improving the quality of service (QoS) with new methodologies. An algorithm based on a bio-inspired model was developed. This algorithm is based on the behavior of gray wolves and it is called Algorithm for 5G Resource Management Optmization in Edge Computing (GROMEC). The algorithm uses the meta-heuristic technique applied to Edge Computing, to result in a better allocation resources through user equipment (UE). The resources considered for allocation in that work are processing, memory, time and storage. Two genetic algorithms were used to define the fitness of an Edge in relation to the resource. Two other algorithms that use traditional techniques in the literature, the Best-First and AHP methods, were considered in the evaluation and comparison with the GROMEC. In the function used to calculate fitness during the simulation made with the GROMEC, the proposed algorithm had a lower number of denied services, presented a low number of blocks and was able to meet the largest number of UEs allocating on average up to 50% more in relation to the Best and 5.25% in relation to Nancy. |
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Repositório Institucional da UNESP |
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Algorithm for 5G Resource Management Optimization in Edge Computingoptimizationresource allocation5Gedge computingThe Internet of Things (IoT) brings new applications and challenges related to cloud computing. The service distribution challenge is becoming more evident and a need for better service options is emerging. The focus of the work is to optimize issues related to the allocation of resources in Edge Computing, improving the quality of service (QoS) with new methodologies. An algorithm based on a bio-inspired model was developed. This algorithm is based on the behavior of gray wolves and it is called Algorithm for 5G Resource Management Optmization in Edge Computing (GROMEC). The algorithm uses the meta-heuristic technique applied to Edge Computing, to result in a better allocation resources through user equipment (UE). The resources considered for allocation in that work are processing, memory, time and storage. Two genetic algorithms were used to define the fitness of an Edge in relation to the resource. Two other algorithms that use traditional techniques in the literature, the Best-First and AHP methods, were considered in the evaluation and comparison with the GROMEC. In the function used to calculate fitness during the simulation made with the GROMEC, the proposed algorithm had a lower number of denied services, presented a low number of blocks and was able to meet the largest number of UEs allocating on average up to 50% more in relation to the Best and 5.25% in relation to Nancy.Univ Estadual Paulista, Sao Jose Rio Preto, Sao Paulo, BrazilUniv Fed Sao Carlos, Sao Paulo, BrazilUniv Estadual Paulista, Sao Jose Rio Preto, Sao Paulo, BrazilIeee-inst Electrical Electronics Engineers IncUniversidade Estadual Paulista (UNESP)Universidade Federal de São Carlos (UFSCar)Lieira, Douglas Dias [UNESP]Quessada, Matheus Sanches [UNESP]Cristiani, Andre LuisMeneguette, Rodolfo Ipolito2022-04-28T17:20:20Z2022-04-28T17:20:20Z2021-10-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article1772-1780Ieee Latin America Transactions. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 19, n. 10, p. 1772-1780, 2021.1548-0992http://hdl.handle.net/11449/218298WOS:000670590700017Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengIeee Latin America Transactionsinfo:eu-repo/semantics/openAccess2022-04-28T17:20:20Zoai:repositorio.unesp.br:11449/218298Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T16:02:39.046749Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Algorithm for 5G Resource Management Optimization in Edge Computing |
title |
Algorithm for 5G Resource Management Optimization in Edge Computing |
spellingShingle |
Algorithm for 5G Resource Management Optimization in Edge Computing Lieira, Douglas Dias [UNESP] optimization resource allocation 5G edge computing |
title_short |
Algorithm for 5G Resource Management Optimization in Edge Computing |
title_full |
Algorithm for 5G Resource Management Optimization in Edge Computing |
title_fullStr |
Algorithm for 5G Resource Management Optimization in Edge Computing |
title_full_unstemmed |
Algorithm for 5G Resource Management Optimization in Edge Computing |
title_sort |
Algorithm for 5G Resource Management Optimization in Edge Computing |
author |
Lieira, Douglas Dias [UNESP] |
author_facet |
Lieira, Douglas Dias [UNESP] Quessada, Matheus Sanches [UNESP] Cristiani, Andre Luis Meneguette, Rodolfo Ipolito |
author_role |
author |
author2 |
Quessada, Matheus Sanches [UNESP] Cristiani, Andre Luis Meneguette, Rodolfo Ipolito |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) Universidade Federal de São Carlos (UFSCar) |
dc.contributor.author.fl_str_mv |
Lieira, Douglas Dias [UNESP] Quessada, Matheus Sanches [UNESP] Cristiani, Andre Luis Meneguette, Rodolfo Ipolito |
dc.subject.por.fl_str_mv |
optimization resource allocation 5G edge computing |
topic |
optimization resource allocation 5G edge computing |
description |
The Internet of Things (IoT) brings new applications and challenges related to cloud computing. The service distribution challenge is becoming more evident and a need for better service options is emerging. The focus of the work is to optimize issues related to the allocation of resources in Edge Computing, improving the quality of service (QoS) with new methodologies. An algorithm based on a bio-inspired model was developed. This algorithm is based on the behavior of gray wolves and it is called Algorithm for 5G Resource Management Optmization in Edge Computing (GROMEC). The algorithm uses the meta-heuristic technique applied to Edge Computing, to result in a better allocation resources through user equipment (UE). The resources considered for allocation in that work are processing, memory, time and storage. Two genetic algorithms were used to define the fitness of an Edge in relation to the resource. Two other algorithms that use traditional techniques in the literature, the Best-First and AHP methods, were considered in the evaluation and comparison with the GROMEC. In the function used to calculate fitness during the simulation made with the GROMEC, the proposed algorithm had a lower number of denied services, presented a low number of blocks and was able to meet the largest number of UEs allocating on average up to 50% more in relation to the Best and 5.25% in relation to Nancy. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-10-01 2022-04-28T17:20:20Z 2022-04-28T17:20:20Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
Ieee Latin America Transactions. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 19, n. 10, p. 1772-1780, 2021. 1548-0992 http://hdl.handle.net/11449/218298 WOS:000670590700017 |
identifier_str_mv |
Ieee Latin America Transactions. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 19, n. 10, p. 1772-1780, 2021. 1548-0992 WOS:000670590700017 |
url |
http://hdl.handle.net/11449/218298 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Ieee Latin America Transactions |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
1772-1780 |
dc.publisher.none.fl_str_mv |
Ieee-inst Electrical Electronics Engineers Inc |
publisher.none.fl_str_mv |
Ieee-inst Electrical Electronics Engineers Inc |
dc.source.none.fl_str_mv |
Web of Science reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808128598198976512 |