Grey Wolf Optimizer-based Task Scheduling for IoT-based Applications in the Edge Computing

Aram Satouf; Ali Hamidoglu; Omer Melih Gul; Alar Kuusik

doi:10.1109/FMEC59375.2023.10306148

Grey Wolf Optimizer-based Task Scheduling for IoT-based Applications in the Edge Computing

Aram Satouf, Ali Hamidoglu, Omer Melih Gul, Alar Kuusik

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

The growing data volume generated by IoT devices places considerable resource constraints on traditional cloud data centers, compromising their ability to cater to delaysensitive IoT applications. The emergence of cloud-fog computing offers a potential solution, by extending cloud resources to the network edge. Yet, task scheduling in the cloud-fog environment introduces new challenges. Our study presents a semi-dynamic real-time task scheduling algorithm developed specifically for the cloud-fog environment, which efficiently allocates tasks while minimizing energy consumption, cost, and makespan. We utilized a modified grey wolf optimizer to optimize task allocation based on parameters like task length, resource requirements, and execution time. Compared to existing methods, including genetic algorithm, our algorithm demonstrates superior performance in terms of makespan, total execution time, cost, and energy consumption.

Original language	English
Title of host publication	2023 8th International Conference on Fog and Mobile Edge Computing, FMEC 2023
Editors	Muhannad Quwaider, Feras M. Awaysheh, Yaser Jararweh
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	52-57
Number of pages	6
ISBN (Electronic)	9798350316971
DOIs	https://doi.org/10.1109/FMEC59375.2023.10306148
Publication status	Published - 2023
Externally published	Yes
Event	8th IEEE International Conference on Fog and Mobile Edge Computing, FMEC 2023 - Tartu, Estonia Duration: 18 Sept 2023 → 20 Sept 2023

Publication series

Name	2023 8th International Conference on Fog and Mobile Edge Computing, FMEC 2023

Conference

Conference	8th IEEE International Conference on Fog and Mobile Edge Computing, FMEC 2023
Country/Territory	Estonia
City	Tartu
Period	18/09/23 → 20/09/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

energy consumption
fog and edge computing
Internet of Things (IoT)
optimization
task scheduling

Access to Document

10.1109/FMEC59375.2023.10306148

Cite this

Satouf, A., Hamidoglu, A., Gul, O. M., & Kuusik, A. (2023). Grey Wolf Optimizer-based Task Scheduling for IoT-based Applications in the Edge Computing. In M. Quwaider, F. M. Awaysheh, & Y. Jararweh (Eds.), 2023 8th International Conference on Fog and Mobile Edge Computing, FMEC 2023 (pp. 52-57). (2023 8th International Conference on Fog and Mobile Edge Computing, FMEC 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/FMEC59375.2023.10306148

Satouf, Aram ; Hamidoglu, Ali ; Gul, Omer Melih et al. / Grey Wolf Optimizer-based Task Scheduling for IoT-based Applications in the Edge Computing. 2023 8th International Conference on Fog and Mobile Edge Computing, FMEC 2023. editor / Muhannad Quwaider ; Feras M. Awaysheh ; Yaser Jararweh. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 52-57 (2023 8th International Conference on Fog and Mobile Edge Computing, FMEC 2023).

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title = "Grey Wolf Optimizer-based Task Scheduling for IoT-based Applications in the Edge Computing",

abstract = "The growing data volume generated by IoT devices places considerable resource constraints on traditional cloud data centers, compromising their ability to cater to delaysensitive IoT applications. The emergence of cloud-fog computing offers a potential solution, by extending cloud resources to the network edge. Yet, task scheduling in the cloud-fog environment introduces new challenges. Our study presents a semi-dynamic real-time task scheduling algorithm developed specifically for the cloud-fog environment, which efficiently allocates tasks while minimizing energy consumption, cost, and makespan. We utilized a modified grey wolf optimizer to optimize task allocation based on parameters like task length, resource requirements, and execution time. Compared to existing methods, including genetic algorithm, our algorithm demonstrates superior performance in terms of makespan, total execution time, cost, and energy consumption.",

keywords = "energy consumption, fog and edge computing, Internet of Things (IoT), optimization, task scheduling",

author = "Aram Satouf and Ali Hamidoglu and Gul, {Omer Melih} and Alar Kuusik",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 8th IEEE International Conference on Fog and Mobile Edge Computing, FMEC 2023 ; Conference date: 18-09-2023 Through 20-09-2023",

year = "2023",

doi = "10.1109/FMEC59375.2023.10306148",

language = "English",

series = "2023 8th International Conference on Fog and Mobile Edge Computing, FMEC 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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editor = "Muhannad Quwaider and Awaysheh, {Feras M.} and Yaser Jararweh",

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Satouf, A, Hamidoglu, A, Gul, OM & Kuusik, A 2023, Grey Wolf Optimizer-based Task Scheduling for IoT-based Applications in the Edge Computing. in M Quwaider, FM Awaysheh & Y Jararweh (eds), 2023 8th International Conference on Fog and Mobile Edge Computing, FMEC 2023. 2023 8th International Conference on Fog and Mobile Edge Computing, FMEC 2023, Institute of Electrical and Electronics Engineers Inc., pp. 52-57, 8th IEEE International Conference on Fog and Mobile Edge Computing, FMEC 2023, Tartu, Estonia, 18/09/23. https://doi.org/10.1109/FMEC59375.2023.10306148

Grey Wolf Optimizer-based Task Scheduling for IoT-based Applications in the Edge Computing. / Satouf, Aram; Hamidoglu, Ali; Gul, Omer Melih et al.
2023 8th International Conference on Fog and Mobile Edge Computing, FMEC 2023. ed. / Muhannad Quwaider; Feras M. Awaysheh; Yaser Jararweh. Institute of Electrical and Electronics Engineers Inc., 2023. p. 52-57 (2023 8th International Conference on Fog and Mobile Edge Computing, FMEC 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Grey Wolf Optimizer-based Task Scheduling for IoT-based Applications in the Edge Computing

AU - Satouf, Aram

AU - Hamidoglu, Ali

AU - Gul, Omer Melih

AU - Kuusik, Alar

PY - 2023

Y1 - 2023

N2 - The growing data volume generated by IoT devices places considerable resource constraints on traditional cloud data centers, compromising their ability to cater to delaysensitive IoT applications. The emergence of cloud-fog computing offers a potential solution, by extending cloud resources to the network edge. Yet, task scheduling in the cloud-fog environment introduces new challenges. Our study presents a semi-dynamic real-time task scheduling algorithm developed specifically for the cloud-fog environment, which efficiently allocates tasks while minimizing energy consumption, cost, and makespan. We utilized a modified grey wolf optimizer to optimize task allocation based on parameters like task length, resource requirements, and execution time. Compared to existing methods, including genetic algorithm, our algorithm demonstrates superior performance in terms of makespan, total execution time, cost, and energy consumption.

AB - The growing data volume generated by IoT devices places considerable resource constraints on traditional cloud data centers, compromising their ability to cater to delaysensitive IoT applications. The emergence of cloud-fog computing offers a potential solution, by extending cloud resources to the network edge. Yet, task scheduling in the cloud-fog environment introduces new challenges. Our study presents a semi-dynamic real-time task scheduling algorithm developed specifically for the cloud-fog environment, which efficiently allocates tasks while minimizing energy consumption, cost, and makespan. We utilized a modified grey wolf optimizer to optimize task allocation based on parameters like task length, resource requirements, and execution time. Compared to existing methods, including genetic algorithm, our algorithm demonstrates superior performance in terms of makespan, total execution time, cost, and energy consumption.

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M3 - Conference contribution

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ER -

Satouf A, Hamidoglu A, Gul OM, Kuusik A. Grey Wolf Optimizer-based Task Scheduling for IoT-based Applications in the Edge Computing. In Quwaider M, Awaysheh FM, Jararweh Y, editors, 2023 8th International Conference on Fog and Mobile Edge Computing, FMEC 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 52-57. (2023 8th International Conference on Fog and Mobile Edge Computing, FMEC 2023). doi: 10.1109/FMEC59375.2023.10306148

Grey Wolf Optimizer-based Task Scheduling for IoT-based Applications in the Edge Computing

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

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