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Intelligent and Converged Networks 2023, 4(4): 273-279 https://doi.org/10.23919/ICN.2023.0022
Open Access | Issue | Published: 30 December 2023

Enhancing resource allocation in edge and fog-cloud computing with genetic algorithm and particle swarm optimization

Show Author's Information Saad-Eddine Chafi1( ),  Younes Balboul1,  Mohammed Fattah2,  Said Mazer1,  Moulhime El Bekkali1
Laboratory of Artificial Intelligence, Data Sciences and Emerging Systems, National School of Applied Sciences, Sidi Mohamed Ben Abdellah University, Fez 30000, Morocco
LIA Laboratory, Moulay Ismail University, Meknes 50050, Morocco
Keywords:
particle swarm optimization, performance evaluation, genetic algorithm, edge and fog cloud, FogWorkflowSim
Cite this article:
Chafi S-E, Balboul Y, Fattah M, et al. Enhancing resource allocation in edge and fog-cloud computing with genetic algorithm and particle swarm optimization. Intelligent and Converged Networks, 2023, 4(4): 273-279. https://doi.org/10.23919/ICN.2023.0022
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Abstract Full text About this article

Evolutionary algorithms have gained significant attention from researchers as effective solutions for various optimization problems. Genetic Algorithm (GA) is widely popular due to its logical approach, broad applicability, and ability to tackle complex issues encountered in engineering systems. However, GA is known for its high implementation cost and typically requires a large number of iterations. On the other hand, Particle Swarm Optimization (PSO) is a relatively new heuristic technique inspired by the collective behaviors of real organisms. Both GA and PSO algorithms are prominent heuristic optimization methods that belong to the population-based approaches family. While they are often seen as competitors, their efficiency heavily relies on the parameter values chosen and the specific optimization problem at hand. In this study, we aim to compare the runtime performance of GA and PSO algorithms within a cutting-edge edge and fog cloud architecture. Through extensive experiments and performance evaluations, the authors demonstrate the effectiveness of GA and PSO algorithms in improving resource allocation in edge and fog cloud computing scenarios using FogWorkflowSim simulator. The comparative analysis sheds light on the strengths and limitations of each algorithm, providing valuable insights for researchers and practitioners in the field.


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About this article

Enhancing resource allocation in edge and fog-cloud computing with genetic algorithm and particle swarm optimization

Show Author's information Saad-Eddine Chafi1( ), Younes Balboul1, Mohammed Fattah2, Said Mazer1, Moulhime El Bekkali1
Laboratory of Artificial Intelligence, Data Sciences and Emerging Systems, National School of Applied Sciences, Sidi Mohamed Ben Abdellah University, Fez 30000, Morocco
LIA Laboratory, Moulay Ismail University, Meknes 50050, Morocco

Abstract

Evolutionary algorithms have gained significant attention from researchers as effective solutions for various optimization problems. Genetic Algorithm (GA) is widely popular due to its logical approach, broad applicability, and ability to tackle complex issues encountered in engineering systems. However, GA is known for its high implementation cost and typically requires a large number of iterations. On the other hand, Particle Swarm Optimization (PSO) is a relatively new heuristic technique inspired by the collective behaviors of real organisms. Both GA and PSO algorithms are prominent heuristic optimization methods that belong to the population-based approaches family. While they are often seen as competitors, their efficiency heavily relies on the parameter values chosen and the specific optimization problem at hand. In this study, we aim to compare the runtime performance of GA and PSO algorithms within a cutting-edge edge and fog cloud architecture. Through extensive experiments and performance evaluations, the authors demonstrate the effectiveness of GA and PSO algorithms in improving resource allocation in edge and fog cloud computing scenarios using FogWorkflowSim simulator. The comparative analysis sheds light on the strengths and limitations of each algorithm, providing valuable insights for researchers and practitioners in the field.

Keywords: particle swarm optimization, performance evaluation, genetic algorithm, edge and fog cloud, FogWorkflowSim

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Received: 10 April 2023
Accepted: 30 May 2023
Published: 30 December 2023
Issue date: December 2023

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