Multi-objective Grey Wolf Optimizer Algorithm for Task Scheduling in Cloud-Fog Computing

Recently, the revolution of IoT and its capabilities to serve various fields that lead to generating a large amount of data, which requires an instant response, especially with sensitive delay tasks. The main challenge is assigning tasks to appropriate resources compatible with task requirements. Be...

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Bibliographic Details
Published in:IEEE access 2023-01, Vol.11, p.1-1
Main Authors: Saif, Faten A., Latip, Rohaya, Hanapi, ZM, Shafinah, K
Format: Article
Language:English
Subjects:
Algorithms
Cloud computing
Cloud-fog computing
Costs
Data centers
Delay
Delays
End users
Energy consumption
Grey wolf optimizer
Heuristic algorithms
Internet of Things
Meta-heuristic
Multiple objective analysis
Processor scheduling
Task analysis
Task complexity
Task Scheduling
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Summary:Recently, the revolution of IoT and its capabilities to serve various fields that lead to generating a large amount of data, which requires an instant response, especially with sensitive delay tasks. The main challenge is assigning tasks to appropriate resources compatible with task requirements. Besides, sending tasks to the fog layer will decrease the delay but increase the users' energy consumption. In contrast, sending tasks to the cloud center will decrease the delay but increase the energy consumption. Thus, this study proposed a Multi-Objectives Optimization (MOP) using the Grey Wolf Optimizer algorithm that cooperate the MOP and the original Grey Wolf Optimizer algorithm to reduce delay and energy consumption. MGWO held in the fog broker which plays an essential role in distributing tasks in the cloud-fog system. The simulation result verifies the effectiveness of the proposed algorithm compared to the related approach in reducing delay and Energy consumption.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2023.3241240