Boosting task scheduling in IoT environments using an improved golden jackal optimization and artificial hummingbird algorithm

Ibrahim Attiya; Mohammed A. A. Al-qaness; Mohamed Abd Elaziz; Ahmad O. Aseeri; Ibrahim Attiya; Mohammed A. A. Al-qaness; Mohamed Abd Elaziz; Ahmad O. Aseeri

doi:10.3934/math.2024043

AIMS Mathematics

2024, Volume 9, Issue 1: 847-867. doi: 10.3934/math.2024043

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Boosting task scheduling in IoT environments using an improved golden jackal optimization and artificial hummingbird algorithm

1.
Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt
2.
Faculty of Computer Science and Engineering, New Mansoura University, New Mansoura, Egypt
3.
College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua 321004, China
4.
Zhejiang Optoelectronics Research Institute, Jinhua 321004, China
5.
Artificial Intelligence Research Center (AIRC), Ajman University, Ajman 346, United Arab Emirates
6.
MEU Research Unit, Middle East University, Amman 11831, Jordan
7.
Department of Electrical and Computer Engineering, Lebanese American University, Byblos 13-5053, Lebanon
8.
Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia

Received: 30 September 2023 Revised: 15 November 2023 Accepted: 22 November 2023 Published: 04 December 2023
MSC : 68M10, 68M11, 68M20, 90C29

Applications for the internet of things (IoT) have grown significantly in popularity in recent years, and this has caused a huge increase in the use of cloud services (CSs). In addition, cloud computing (CC) efficiently processes and stores generated application data, which is evident in the lengthened response times of sensitive applications. Moreover, CC bandwidth limitations and power consumption are still unresolved issues. In order to balance CC, fog computing (FC) has been developed. FC broadens its offering of CSs to target end users and edge devices. Due to its low processing capability, FC only handles light activities; jobs that require more time will be done via CC. This study presents an alternative task scheduling in an IoT environment based on improving the performance of the golden jackal optimization (GJO) using the artificial hummingbird algorithm (AHA). To test the effectiveness of the developed task scheduling technique named golden jackal artificial hummingbird (GJAH), we conducted a large number of experiments on two separate datasets with varying data sizing. The GJAH algorithm provides better performance than those competitive task scheduling methods. In particular, GJAH can schedule and carry out activities more effectively than other algorithms to reduce the makespan time and energy consumption in a cloud-fog computing environment.
- internet of things (IoT),
- golden jackal optimization (GJO),
- artificial hummingbird algorithm (AHA),
- energy consumption,
- cloud computing
Citation: Ibrahim Attiya, Mohammed A. A. Al-qaness, Mohamed Abd Elaziz, Ahmad O. Aseeri. Boosting task scheduling in IoT environments using an improved golden jackal optimization and artificial hummingbird algorithm[J]. AIMS Mathematics, 2024, 9(1): 847-867. doi: 10.3934/math.2024043

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Abstract

Applications for the internet of things (IoT) have grown significantly in popularity in recent years, and this has caused a huge increase in the use of cloud services (CSs). In addition, cloud computing (CC) efficiently processes and stores generated application data, which is evident in the lengthened response times of sensitive applications. Moreover, CC bandwidth limitations and power consumption are still unresolved issues. In order to balance CC, fog computing (FC) has been developed. FC broadens its offering of CSs to target end users and edge devices. Due to its low processing capability, FC only handles light activities; jobs that require more time will be done via CC. This study presents an alternative task scheduling in an IoT environment based on improving the performance of the golden jackal optimization (GJO) using the artificial hummingbird algorithm (AHA). To test the effectiveness of the developed task scheduling technique named golden jackal artificial hummingbird (GJAH), we conducted a large number of experiments on two separate datasets with varying data sizing. The GJAH algorithm provides better performance than those competitive task scheduling methods. In particular, GJAH can schedule and carry out activities more effectively than other algorithms to reduce the makespan time and energy consumption in a cloud-fog computing environment.

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