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

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
• genetic algorithm
• performance evaluation
• edge and fog cloud
• fogworkflowsim