An approach to improve the quality of services using a multi-objective differential evolution algorithm in fog computing

Abstract

Abstract With the development of the Internet of Things (IoT) technology, a vast amount of the IoT data is generated by mobile applications from mobile devices. Cloudlets provide a paradigm that allows the mobile applications and the generated IoT data to be offloaded from the mobile devices to the cloudlets for processing and storage through the access points (APs) in the Wireless Metropolitan Area Networks (WMANs). However, achieving the goal of optimizing resource utilization, latency, and reliability for WMAN with cloudlet Usability is still a challenge, which in this dissertation aims to optimize these targets with cloudlet Usability. In this dissertation, load unloading strategy in wireless networks of urban areas is analyzed and modeled as a multi-objective optimization problem. Multi-objective problem solving is optimized by the NSDE (Nondominated Sorting Differential Evolution) algorithm and the diversity and convergence of the population are ensured through the mutation and crossover operations. In the individual selection phase, NSDE uses the fast nondominated sorting approach and the crowded-comparison operator to ensure that individuals with the relatively best fitness values in the current population can be preserved for the next generation. Finally, the results of the proposed method were compared and evaluated based on the three criteria of resource utilization, latency and reliability with the results obtained from the genetic algorithm and particle swarm accumulation and Hungarian Algorithm. Experimental results show that the proposed method is effective and efficient. The accuracy of the proposed method compared to the Hungarian algorithm with the criteria of resource utilization and latency and reliability are 23.2%, 26.6% and 21.8%, respectively, and compared to the genetic algorithm is 5.8%, 12.4% and 11.3%, respectively.