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Título : Computational resource allocation in fog computing : a comprehensive survey
Autor : Bachiega Junior, João
Costa, Breno Gustavo Soares da
Carvalho, Leonardo Rebouças de
Rosa, Michel Junio Ferreira
Araújo, Aletéia Patrícia Favacho de
Assunto:: Computação em nuvem
Alocação de recursos
Fecha de publicación : 17-jul-2023
Editorial : the Association for Computing Machinery
Citación : BACHIEGA JUNIOR, João et al. Computational resource allocation in fog computing : a comprehensive survey. ACM Computing Surveys, v. 55, n. 14s, art. 336, 2023. DOI: https://doi.org/10.1145/3586181. Disponível em: https://dl.acm.org/doi/10.1145/3586181. Acesso em: 21 maio 2024.
Abstract: Fog computing is a paradigm that allows the provisioning of computational resources and services at the edge of the network, closer to the end devices and users, complementing cloud computing. The heterogeneity and large number of devices are challenges to obtaining optimized resource allocation in this environment. Over time, some surveys have been presented on resource management in fog computing. However, they now lack a broader and deeper view about this subject, considering the recent publications. This article presents a systematic literature review with a focus on resource allocation for fog computing, and in a more comprehensive way than the existing works. The survey is based on 108 selected publications from 2012 to 2022. The analysis has exposed their main techniques, metrics used, evaluation tools, virtualization methods, architecture, and domains where the proposed solutions were applied. The results show an updated and comprehensive view about resource allocation in fog computing. The main challenges and open research questions are discussed, and a new fog computing resource management cycle is proposed.
metadata.dc.description.unidade: Instituto de Ciências Exatas (IE)
Departamento de Ciência da Computação (IE CIC)
metadata.dc.description.ppg: Programa de Pós-Graduação em Informática
DOI: https://doi.org/10.1145/3586181
metadata.dc.relation.publisherversion: https://dl.acm.org/doi/10.1145/3586181
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