Resource Allocation Technique for Edge Computing using Grey Wolf Optimization Algorithm

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorFed Inst Sao Paulo IFSP-
Autor(es): dc.contributorUniversidade Federal de São Carlos (UFSCar)-
Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.creatorLieira, Douglas D.-
Autor(es): dc.creatorQuessada, Matheus S.-
Autor(es): dc.creatorCristiani, Andre L.-
Autor(es): dc.creatorMeneguette, Rodolfo I.-
Autor(es): dc.creatorVelazquez, R.-
Data de aceite: dc.date.accessioned2025-08-21T19:29:46Z-
Data de disponibilização: dc.date.available2025-08-21T19:29:46Z-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2019-12-31-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/245189-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/245189-
Descrição: dc.descriptionThe 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.-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionSao Paulo State Univ, Sao Jose Do Rio Preto, SP, Brazil-
Descrição: dc.descriptionFed Inst Sao Paulo IFSP, Catanduva, SP, Brazil-
Descrição: dc.descriptionFed Univ Sao Carlos UFSCAR, Sao Carlos, SP, Brazil-
Descrição: dc.descriptionUniv Sao Paulo, Sao Carlos, SP, Brazil-
Descrição: dc.descriptionSao Paulo State Univ, Sao Jose Do Rio Preto, SP, Brazil-
Descrição: dc.descriptionCNPq: 407248/2018-8-
Descrição: dc.descriptionCNPq: 309822/2018-1-
Formato: dc.format6-
Idioma: dc.languageen-
Publicador: dc.publisherIeee-
Relação: dc.relation2020 Ieee Latin-american Conference On Communications (latincom 2020)-
???dc.source???: dc.sourceWeb of Science-
Palavras-chave: dc.subjectresource allocation-
Palavras-chave: dc.subjectedge computing-
Palavras-chave: dc.subjectmeta-heuristic-
Título: dc.titleResource Allocation Technique for Edge Computing using Grey Wolf Optimization Algorithm-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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