Non-dominated Sorting Genetic Optimization-based Fog Cloudlet Computing for Wireless Metropolitan Area Networks

Abstract

Fog cloud computing management is an emerging topic. It involves accepting generated tasks from users and assigning them in the fog nodes or sending them to the cloud with optimizing various objectives. One specific type of fog environment is cloudlet which indicates to local servers deployed in wireless metropolitan area network and leveraging the existing access points or base-station for communicating tasks between users and cloudlets. The existing methods of cloudlet-based computing optimization are numerous. This article provides cloudlet computing management process based on multi-objective optimization using the concept of non-dominated sorting. The considered objectives are latency, energy consumption at the cloudlets, energy consumption at the user, and cost which is calculated based on the number of cloudlets. Non-dominated sorting genetic algorithms (NSGA-II) and (NSGA-III) are used for comparison. The results reveal equivalent performance between them with slight superiority of NSGA-III over NSGA-II in terms of the number of non-dominated solutions.