- Title
- An optimal scheduling method in iot-fog-cloud network using combination of aquila optimizer and african vultures optimization
- Creator
- Liu, Qing; Kosarirad, Houman; Meisami, Sajad; Alnowibet, Khalid; Hoshyar, Azadeh
- Date
- 2023
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/194443
- Identifier
- vital:18357
- Identifier
-
https://doi.org/10.3390/pr11041162
- Identifier
- ISSN:2227-9717 (ISSN)
- Abstract
- Today, fog and cloud computing environments can be used to further develop the Internet of Things (IoT). In such environments, task scheduling is very efficient for executing user requests, and the optimal scheduling of IoT task requests increases the productivity of the IoT-fog-cloud system. In this paper, a hybrid meta-heuristic (MH) algorithm is developed to schedule the IoT requests in IoT-fog-cloud networks using the Aquila Optimizer (AO) and African Vultures Optimization Algorithm (AVOA) called AO_AVOA. In AO_AVOA, the exploration phase of AVOA is improved by using AO operators to obtain the best solution during the process of finding the optimal scheduling solution. A comparison between AO_AVOA and methods of AVOA, AO, Firefly Algorithm (FA), particle swarm optimization (PSO), and Harris Hawks Optimization (HHO) according to performance metrics such as makespan and throughput shows the high ability of AO_AVOA to solve the scheduling problem in IoT-fog-cloud networks. © 2023 by the authors.
- Publisher
- MDPI
- Relation
- Processes Vol. 11, no. 4 (2023), p.
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- https://creativecommons.org/licenses/by/4.0/
- Rights
- Copyright © 2023 by the authors
- Rights
- Open Access
- Subject
- 4004 Chemical engineering; African vultures optimization algorithm; Aquila optimizer; cloud computing; fog computing; Internet of Things; Task scheduling
- Full Text
- Reviewed
- Funder
- The authors extend their appreciation to King Saud University, Saudi Arabia for funding this work through Researchers Supporting Project number (RSP2023R305), King Saud University, Riyadh, Saudi Arabia.
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