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Multi-objectives firefly algorithm for task offloading in the edge-fog-cloud computing

This study proposes a two-stage strategy to ensure the successful execution of critical tasks. In the first stage, an Enhanced Task Offloading (ETO) algorithm is introduced to determine the appropriate computing layer—edge, fog, or cloud—for offloading incomplete tasks. The algorithm makes this d...

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Main Authors: A. Saif, Faten, Latip, Rohaya, Mohd Hanapi, Zurina, Kamarudin, Shafinah, Senthil Kumar, A.V., Salem Bajaher, Awadh
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers 2024
Subjects:
Cloud computing
Mobile communication systems
Online Access:http://psasir.upm.edu.my/id/eprint/114425/1/114425.pdf
http://psasir.upm.edu.my/id/eprint/114425/
https://ieeexplore.ieee.org/document/10738798/
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spelling my.upm.eprints.1144252025-01-20T02:03:52Z http://psasir.upm.edu.my/id/eprint/114425/ Multi-objectives firefly algorithm for task offloading in the edge-fog-cloud computing A. Saif, Faten Latip, Rohaya Mohd Hanapi, Zurina Kamarudin, Shafinah Senthil Kumar, A.V. Salem Bajaher, Awadh This study proposes a two-stage strategy to ensure the successful execution of critical tasks. In the first stage, an Enhanced Task Offloading (ETO) algorithm is introduced to determine the appropriate computing layer—edge, fog, or cloud—for offloading incomplete tasks. The algorithm makes this decision by assessing the availability of idle computing resources relative to the task’s computational requirements. Additionally, it verifies the status of the server (on/off) before offloading; if the server is unavailable, the algorithm proceeds to check the next layer. In the second stage, the strategy employs a Multi-objective Firefly (MFA) algorithm to assign the optimal computational device within the selected layer. Experimental simulations compare the proposed strategy with a benchmark task offloading algorithm. The results demonstrate the superiority of the proposed strategy, achieving reductions in energy consumption and delay and maximizing resource utilization compared to the baseline algorithms. Institute of Electrical and Electronics Engineers 2024-10-30 Article PeerReviewed text en cc_by_nc_nd_4 http://psasir.upm.edu.my/id/eprint/114425/1/114425.pdf A. Saif, Faten and Latip, Rohaya and Mohd Hanapi, Zurina and Kamarudin, Shafinah and Senthil Kumar, A.V. and Salem Bajaher, Awadh (2024) Multi-objectives firefly algorithm for task offloading in the edge-fog-cloud computing. IEEE Access, 12. pp. 159561-159578. ISSN 2169-3536 https://ieeexplore.ieee.org/document/10738798/ Cloud computing Mobile communication systems 10.1109/ACCESS.2024.3488032
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
topic Cloud computing
Mobile communication systems
spellingShingle Cloud computing
Mobile communication systems
A. Saif, Faten
Latip, Rohaya
Mohd Hanapi, Zurina
Kamarudin, Shafinah
Senthil Kumar, A.V.
Salem Bajaher, Awadh
Multi-objectives firefly algorithm for task offloading in the edge-fog-cloud computing
description This study proposes a two-stage strategy to ensure the successful execution of critical tasks. In the first stage, an Enhanced Task Offloading (ETO) algorithm is introduced to determine the appropriate computing layer—edge, fog, or cloud—for offloading incomplete tasks. The algorithm makes this decision by assessing the availability of idle computing resources relative to the task’s computational requirements. Additionally, it verifies the status of the server (on/off) before offloading; if the server is unavailable, the algorithm proceeds to check the next layer. In the second stage, the strategy employs a Multi-objective Firefly (MFA) algorithm to assign the optimal computational device within the selected layer. Experimental simulations compare the proposed strategy with a benchmark task offloading algorithm. The results demonstrate the superiority of the proposed strategy, achieving reductions in energy consumption and delay and maximizing resource utilization compared to the baseline algorithms.
format Article
author A. Saif, Faten
Latip, Rohaya
Mohd Hanapi, Zurina
Kamarudin, Shafinah
Senthil Kumar, A.V.
Salem Bajaher, Awadh
author_facet A. Saif, Faten
Latip, Rohaya
Mohd Hanapi, Zurina
Kamarudin, Shafinah
Senthil Kumar, A.V.
Salem Bajaher, Awadh
author_sort A. Saif, Faten
title Multi-objectives firefly algorithm for task offloading in the edge-fog-cloud computing
title_short Multi-objectives firefly algorithm for task offloading in the edge-fog-cloud computing
title_full Multi-objectives firefly algorithm for task offloading in the edge-fog-cloud computing
title_fullStr Multi-objectives firefly algorithm for task offloading in the edge-fog-cloud computing
title_full_unstemmed Multi-objectives firefly algorithm for task offloading in the edge-fog-cloud computing
title_sort multi-objectives firefly algorithm for task offloading in the edge-fog-cloud computing
publisher Institute of Electrical and Electronics Engineers
publishDate 2024
url http://psasir.upm.edu.my/id/eprint/114425/1/114425.pdf
http://psasir.upm.edu.my/id/eprint/114425/
https://ieeexplore.ieee.org/document/10738798/
_version_ 1823093152431472640
score 13.23648

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