A Resource Management Model for Distributed Multi-Task Applications in Fog Computing Networks

While the effectiveness of fog computing in Internet of Things (IoT) applications has been widely investigated in various studies, there is still a lack of techniques to efficiently utilize the computing resources in a fog platform to maximize Quality of Service (QoS) and Quality of Experience (QoE)...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Farhoud Hosseinpour, Ahmad Naebi, Seppo Virtanen, Tapio Pahikkala, Hannu Tenhunen, Juha Plosila
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Greedy fog computing Internet of Things modelling optimization resource management Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/0f252ff2db1a438eb308451a7bbb56d7
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:0f252ff2db1a438eb308451a7bbb56d7
record_format	dspace
spelling	oai:doaj.org-article:0f252ff2db1a438eb308451a7bbb56d72021-11-20T00:00:57ZA Resource Management Model for Distributed Multi-Task Applications in Fog Computing Networks2169-353610.1109/ACCESS.2021.3127355https://doaj.org/article/0f252ff2db1a438eb308451a7bbb56d72021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9611264/https://doaj.org/toc/2169-3536While the effectiveness of fog computing in Internet of Things (IoT) applications has been widely investigated in various studies, there is still a lack of techniques to efficiently utilize the computing resources in a fog platform to maximize Quality of Service (QoS) and Quality of Experience (QoE). This paper presents a resource management model for service placement of distributed multitasking applications in fog computing through mathematical modeling of such a platform. Our main design goal is to reduce communication between the candidate nodes hosting different task modules of an application by selecting a group of nodes near each other and as close to the source of the data as possible. We propose a method based on a greedy principle that demonstrates a highly scalable and near-optimal performance for resource mapping problems for multitasking applications in fog computing networks. Compared with the commercial Gurobi optimizer, our proposed algorithm provides a mapping solution that obtains 93% of the performance, attributed to a higher communication cost, while outperforming the reference method in terms of the computing speed, cutting the mapping execution time to less than 1% of that of the Gurobi optimizer.Farhoud HosseinpourAhmad NaebiSeppo VirtanenTapio PahikkalaHannu TenhunenJuha PlosilaIEEEarticleGreedyfog computingInternet of Thingsmodellingoptimizationresource managementElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 152792-152802 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Greedy fog computing Internet of Things modelling optimization resource management Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Greedy fog computing Internet of Things modelling optimization resource management Electrical engineering. Electronics. Nuclear engineering TK1-9971 Farhoud Hosseinpour Ahmad Naebi Seppo Virtanen Tapio Pahikkala Hannu Tenhunen Juha Plosila A Resource Management Model for Distributed Multi-Task Applications in Fog Computing Networks
description	While the effectiveness of fog computing in Internet of Things (IoT) applications has been widely investigated in various studies, there is still a lack of techniques to efficiently utilize the computing resources in a fog platform to maximize Quality of Service (QoS) and Quality of Experience (QoE). This paper presents a resource management model for service placement of distributed multitasking applications in fog computing through mathematical modeling of such a platform. Our main design goal is to reduce communication between the candidate nodes hosting different task modules of an application by selecting a group of nodes near each other and as close to the source of the data as possible. We propose a method based on a greedy principle that demonstrates a highly scalable and near-optimal performance for resource mapping problems for multitasking applications in fog computing networks. Compared with the commercial Gurobi optimizer, our proposed algorithm provides a mapping solution that obtains 93% of the performance, attributed to a higher communication cost, while outperforming the reference method in terms of the computing speed, cutting the mapping execution time to less than 1% of that of the Gurobi optimizer.
format	article
author	Farhoud Hosseinpour Ahmad Naebi Seppo Virtanen Tapio Pahikkala Hannu Tenhunen Juha Plosila
author_facet	Farhoud Hosseinpour Ahmad Naebi Seppo Virtanen Tapio Pahikkala Hannu Tenhunen Juha Plosila
author_sort	Farhoud Hosseinpour
title	A Resource Management Model for Distributed Multi-Task Applications in Fog Computing Networks
title_short	A Resource Management Model for Distributed Multi-Task Applications in Fog Computing Networks
title_full	A Resource Management Model for Distributed Multi-Task Applications in Fog Computing Networks
title_fullStr	A Resource Management Model for Distributed Multi-Task Applications in Fog Computing Networks
title_full_unstemmed	A Resource Management Model for Distributed Multi-Task Applications in Fog Computing Networks
title_sort	resource management model for distributed multi-task applications in fog computing networks
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/0f252ff2db1a438eb308451a7bbb56d7
work_keys_str_mv	AT farhoudhosseinpour aresourcemanagementmodelfordistributedmultitaskapplicationsinfogcomputingnetworks AT ahmadnaebi aresourcemanagementmodelfordistributedmultitaskapplicationsinfogcomputingnetworks AT seppovirtanen aresourcemanagementmodelfordistributedmultitaskapplicationsinfogcomputingnetworks AT tapiopahikkala aresourcemanagementmodelfordistributedmultitaskapplicationsinfogcomputingnetworks AT hannutenhunen aresourcemanagementmodelfordistributedmultitaskapplicationsinfogcomputingnetworks AT juhaplosila aresourcemanagementmodelfordistributedmultitaskapplicationsinfogcomputingnetworks AT farhoudhosseinpour resourcemanagementmodelfordistributedmultitaskapplicationsinfogcomputingnetworks AT ahmadnaebi resourcemanagementmodelfordistributedmultitaskapplicationsinfogcomputingnetworks AT seppovirtanen resourcemanagementmodelfordistributedmultitaskapplicationsinfogcomputingnetworks AT tapiopahikkala resourcemanagementmodelfordistributedmultitaskapplicationsinfogcomputingnetworks AT hannutenhunen resourcemanagementmodelfordistributedmultitaskapplicationsinfogcomputingnetworks AT juhaplosila resourcemanagementmodelfordistributedmultitaskapplicationsinfogcomputingnetworks
_version_	1718419818993942528

A Resource Management Model for Distributed Multi-Task Applications in Fog Computing Networks

Ejemplares similares