Resource Allocation Technique for Edge Computing Using Grey Wolf Optimization Algorithm
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/LATINCOM50620.2020.9282316 http://hdl.handle.net/11449/221646 |
Resumo: | The explosion of IoT technology poses new challenges for researchers in the concept of cloud computing, mainly in improving the distribution of services, which need to be provided with greater efficiency and less latency. Therefore, this work seeks to optimize the methodology of resource allocation in Edge Computing, seeking to improve the quality of service (QoS) to the user. For this, it was developed an algorithm for efficient resource allocation using grey wolves optimization technique, named as Resource Allocation Technique for Edge Computing (RATEC). The algorithm adopted the meta-heuristic technique to choose the best Edge when allocating the resources of user equipment (UE). In this work, it was considered that the UEs are composed of processing, storage, time and memory resources. The algorithm uses these resources to calculate the fitness of each Edge and decide which one to allocate, if available. The RATEC has been compared with two other policies and has managed to serve a number most significant of UEs, reducing the number of services refused and presenting a low number of blockages while searching for an Edge. |
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Repositório Institucional da UNESP |
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Resource Allocation Technique for Edge Computing Using Grey Wolf Optimization Algorithmedge computingmetaheuristicresource allocationThe explosion of IoT technology poses new challenges for researchers in the concept of cloud computing, mainly in improving the distribution of services, which need to be provided with greater efficiency and less latency. Therefore, this work seeks to optimize the methodology of resource allocation in Edge Computing, seeking to improve the quality of service (QoS) to the user. For this, it was developed an algorithm for efficient resource allocation using grey wolves optimization technique, named as Resource Allocation Technique for Edge Computing (RATEC). The algorithm adopted the meta-heuristic technique to choose the best Edge when allocating the resources of user equipment (UE). In this work, it was considered that the UEs are composed of processing, storage, time and memory resources. The algorithm uses these resources to calculate the fitness of each Edge and decide which one to allocate, if available. The RATEC has been compared with two other policies and has managed to serve a number most significant of UEs, reducing the number of services refused and presenting a low number of blockages while searching for an Edge.Federal Institute of São Paulo (IFSP)Sao Paulo State University Sao Jose Do Rio PretoFederal University of São Carlos (UFSCAR) São CarlosUniversity of São Paulo (USP) São CarlosSao Paulo State University Sao Jose Do Rio PretoFederal Institute of São Paulo (IFSP)Universidade Estadual Paulista (UNESP)Universidade Federal de São Carlos (UFSCar)Universidade de São Paulo (USP)Lieira, Douglas D.Quessada, Matheus S. [UNESP]Cristiani, Andre L.Meneguette, Rodolfo I.2022-04-28T19:29:54Z2022-04-28T19:29:54Z2020-11-18info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjecthttp://dx.doi.org/10.1109/LATINCOM50620.2020.9282316Proceedings - 2020 IEEE Latin-American Conference on Communications, LATINCOM 2020.http://hdl.handle.net/11449/22164610.1109/LATINCOM50620.2020.92823162-s2.0-85099238836Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengProceedings - 2020 IEEE Latin-American Conference on Communications, LATINCOM 2020info:eu-repo/semantics/openAccess2022-04-28T19:29:54Zoai:repositorio.unesp.br:11449/221646Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462022-04-28T19:29:54Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Resource Allocation Technique for Edge Computing Using Grey Wolf Optimization Algorithm |
title |
Resource Allocation Technique for Edge Computing Using Grey Wolf Optimization Algorithm |
spellingShingle |
Resource Allocation Technique for Edge Computing Using Grey Wolf Optimization Algorithm Lieira, Douglas D. edge computing metaheuristic resource allocation |
title_short |
Resource Allocation Technique for Edge Computing Using Grey Wolf Optimization Algorithm |
title_full |
Resource Allocation Technique for Edge Computing Using Grey Wolf Optimization Algorithm |
title_fullStr |
Resource Allocation Technique for Edge Computing Using Grey Wolf Optimization Algorithm |
title_full_unstemmed |
Resource Allocation Technique for Edge Computing Using Grey Wolf Optimization Algorithm |
title_sort |
Resource Allocation Technique for Edge Computing Using Grey Wolf Optimization Algorithm |
author |
Lieira, Douglas D. |
author_facet |
Lieira, Douglas D. Quessada, Matheus S. [UNESP] Cristiani, Andre L. Meneguette, Rodolfo I. |
author_role |
author |
author2 |
Quessada, Matheus S. [UNESP] Cristiani, Andre L. Meneguette, Rodolfo I. |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Federal Institute of São Paulo (IFSP) Universidade Estadual Paulista (UNESP) Universidade Federal de São Carlos (UFSCar) Universidade de São Paulo (USP) |
dc.contributor.author.fl_str_mv |
Lieira, Douglas D. Quessada, Matheus S. [UNESP] Cristiani, Andre L. Meneguette, Rodolfo I. |
dc.subject.por.fl_str_mv |
edge computing metaheuristic resource allocation |
topic |
edge computing metaheuristic resource allocation |
description |
The explosion of IoT technology poses new challenges for researchers in the concept of cloud computing, mainly in improving the distribution of services, which need to be provided with greater efficiency and less latency. Therefore, this work seeks to optimize the methodology of resource allocation in Edge Computing, seeking to improve the quality of service (QoS) to the user. For this, it was developed an algorithm for efficient resource allocation using grey wolves optimization technique, named as Resource Allocation Technique for Edge Computing (RATEC). The algorithm adopted the meta-heuristic technique to choose the best Edge when allocating the resources of user equipment (UE). In this work, it was considered that the UEs are composed of processing, storage, time and memory resources. The algorithm uses these resources to calculate the fitness of each Edge and decide which one to allocate, if available. The RATEC has been compared with two other policies and has managed to serve a number most significant of UEs, reducing the number of services refused and presenting a low number of blockages while searching for an Edge. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-11-18 2022-04-28T19:29:54Z 2022-04-28T19:29:54Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1109/LATINCOM50620.2020.9282316 Proceedings - 2020 IEEE Latin-American Conference on Communications, LATINCOM 2020. http://hdl.handle.net/11449/221646 10.1109/LATINCOM50620.2020.9282316 2-s2.0-85099238836 |
url |
http://dx.doi.org/10.1109/LATINCOM50620.2020.9282316 http://hdl.handle.net/11449/221646 |
identifier_str_mv |
Proceedings - 2020 IEEE Latin-American Conference on Communications, LATINCOM 2020. 10.1109/LATINCOM50620.2020.9282316 2-s2.0-85099238836 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Proceedings - 2020 IEEE Latin-American Conference on Communications, LATINCOM 2020 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus 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 |
|
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1799964703976325120 |