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)...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9611264/ |
| _version_ | 1818987280307585024 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-20T19:04:11Z |
| format | Article |
| id | doaj.art-0f252ff2db1a438eb308451a7bbb56d7 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T19:04:11Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-0f252ff2db1a438eb308451a7bbb56d72022-12-21T19:29:19ZengIEEEIEEE Access2169-35362021-01-01915279215280210.1109/ACCESS.2021.31273559611264A Resource Management Model for Distributed Multi-Task Applications in Fog Computing NetworksFarhoud Hosseinpour0https://orcid.org/0000-0003-0961-006XAhmad Naebi1https://orcid.org/0000-0001-9577-9528Seppo Virtanen2https://orcid.org/0000-0002-9487-3018Tapio Pahikkala3https://orcid.org/0000-0003-4183-2455Hannu Tenhunen4Juha Plosila5https://orcid.org/0000-0003-4018-5495Department of Computing, University of Turku (UTU), Turku, FinlandSystem Engineering Institute, Xi’an Jiaotong University, Xi’an, ChinaDepartment of Computing, University of Turku (UTU), Turku, FinlandDepartment of Computing, University of Turku (UTU), Turku, FinlandDepartment of Computing, University of Turku (UTU), Turku, FinlandDepartment of Computing, University of Turku (UTU), Turku, FinlandWhile 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.https://ieeexplore.ieee.org/document/9611264/Greedyfog computingInternet of Thingsmodellingoptimizationresource management |
| spellingShingle | 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 IEEE Access Greedy fog computing Internet of Things modelling optimization resource management |
| title | 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_short | 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 |
| topic | Greedy fog computing Internet of Things modelling optimization resource management |
| url | https://ieeexplore.ieee.org/document/9611264/ |
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