A new approach for service activation management in fog computing using Cat Swarm Optimization algorithm

Abstract

Today, with the increasing expansion of IoT devices and the growing number of user requests, processing their demands in computational environments has become increasingly challenging.The large volume of user requests and the appropriate distribution of tasks among computational resources often result in disordered energy consumption and increased latency. The correct allocation of resources and reducing energy consumption in fog computing are still significant challenges in this field. Improving resource management methods can provide better services for users. In this article, with the aim of more efficient allocation of resources and service activation management, the metaheuristic algorithm CSO (Cat Swarm Optimization) is used. User requests are received by a request evaluator, prioritized, and efficiently executed using the container live migration technique on fog resources. The container live migration technique leads to the migration of services and their better placement on fog resources, avoiding unnecessary activation of physical resources. The proposed method uses a resource manager to identify and classify available resources, aiming to determine the initial capacity of physical fog resources. The performance of the proposed method has been tested and evaluated using six metaheuristic algorithms, namely Particle Swarm Optimization (PSO), Ant Colony Optimization, Grasshopper Optimization algorithm, Genetic algorithm, Cuckoo Optimization algorithm, and Gray Wolf Optimization, within iFogSim. The proposed method has shown superior efficiency in energy consumption, execution time, latency, and network lifetime compared to other algorithms.