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Multi-objective grey wolf optimizer algorithm for task scheduling in cloud-fog computing

The revolution of IoT and its capabilities to serve various fields led to generating a large amount of data for processing. Tasks that require an instant response, especially with sensitive delay tasks send to the fog node due to the close distance, and the complex tasks transfer to the cloud data c...

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Main Authors: Saif, Faten A., Latip, Rohaya, Hanapi, Zurina Mohd, Shafinah, Kamarudin
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers 2023
Online Access:http://psasir.upm.edu.my/id/eprint/109572/1/109572.pdf
http://psasir.upm.edu.my/id/eprint/109572/
https://ieeexplore.ieee.org/document/10032546
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spelling my.upm.eprints.1095722025-01-09T02:27:43Z http://psasir.upm.edu.my/id/eprint/109572/ Multi-objective grey wolf optimizer algorithm for task scheduling in cloud-fog computing Saif, Faten A. Latip, Rohaya Hanapi, Zurina Mohd Shafinah, Kamarudin The revolution of IoT and its capabilities to serve various fields led to generating a large amount of data for processing. Tasks that require an instant response, especially with sensitive delay tasks send to the fog node due to the close distance, and the complex tasks transfer to the cloud data center for its huge computation and storage. However, sending tasks to the fog decreases the transmission delay. Still, it increases the energy consumption of the end users, while transferring tasks to the cloud reduces users’ energy consumption but increases the transmission delay due to the long distance; besides, assigning tasks to appropriate resources compatible with task requirements. These are the main challenges in cloudfog computing that need to improve. Thus, this study proposed a Multi-Objectives Grey Wolf Optimizer (MGWO) algorithm to reduce the QoS objectives delay and energy consumption and held in the fog broker, which plays an essential role in distributing tasks. The simulation result verifies the effectiveness of the MGWO algorithm compared to the state-of-the-art algorithms in reducing delay and Energy consumption. Institute of Electrical and Electronics Engineers 2023-01-31 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/109572/1/109572.pdf Saif, Faten A. and Latip, Rohaya and Hanapi, Zurina Mohd and Shafinah, Kamarudin (2023) Multi-objective grey wolf optimizer algorithm for task scheduling in cloud-fog computing. IEEE Access, 11. pp. 20635-20646. ISSN 2169-3536 https://ieeexplore.ieee.org/document/10032546 10.1109/access.2023.3241240
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
description The revolution of IoT and its capabilities to serve various fields led to generating a large amount of data for processing. Tasks that require an instant response, especially with sensitive delay tasks send to the fog node due to the close distance, and the complex tasks transfer to the cloud data center for its huge computation and storage. However, sending tasks to the fog decreases the transmission delay. Still, it increases the energy consumption of the end users, while transferring tasks to the cloud reduces users’ energy consumption but increases the transmission delay due to the long distance; besides, assigning tasks to appropriate resources compatible with task requirements. These are the main challenges in cloudfog computing that need to improve. Thus, this study proposed a Multi-Objectives Grey Wolf Optimizer (MGWO) algorithm to reduce the QoS objectives delay and energy consumption and held in the fog broker, which plays an essential role in distributing tasks. The simulation result verifies the effectiveness of the MGWO algorithm compared to the state-of-the-art algorithms in reducing delay and Energy consumption.
format Article
author Saif, Faten A.
Latip, Rohaya
Hanapi, Zurina Mohd
Shafinah, Kamarudin
spellingShingle Saif, Faten A.
Latip, Rohaya
Hanapi, Zurina Mohd
Shafinah, Kamarudin
Multi-objective grey wolf optimizer algorithm for task scheduling in cloud-fog computing
author_facet Saif, Faten A.
Latip, Rohaya
Hanapi, Zurina Mohd
Shafinah, Kamarudin
author_sort Saif, Faten A.
title Multi-objective grey wolf optimizer algorithm for task scheduling in cloud-fog computing
title_short Multi-objective grey wolf optimizer algorithm for task scheduling in cloud-fog computing
title_full Multi-objective grey wolf optimizer algorithm for task scheduling in cloud-fog computing
title_fullStr Multi-objective grey wolf optimizer algorithm for task scheduling in cloud-fog computing
title_full_unstemmed Multi-objective grey wolf optimizer algorithm for task scheduling in cloud-fog computing
title_sort multi-objective grey wolf optimizer algorithm for task scheduling in cloud-fog computing
publisher Institute of Electrical and Electronics Engineers
publishDate 2023
url http://psasir.upm.edu.my/id/eprint/109572/1/109572.pdf
http://psasir.upm.edu.my/id/eprint/109572/
https://ieeexplore.ieee.org/document/10032546
_version_ 1821003740186935296
score 13.226378

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