Multi-Objective Task Scheduling Approach for Fog Computing

Despite the remarkable work conducted to improve fog computing applications’ efficiency, task scheduling problem in such an environment is still a big challenge. Optimizing these applications, i.e. critical healthcare smart cities, and transportation urgent save energy, quality of service, reduce carbon emission rate, flow time. As proposed much recent work, dealing with this as single objective did not get desired results. result, paper presents new multi-objective approach based on integrating marine predator’s algorithm polynomial mutation mechanism (MHMPA) for environments. In algorithm, trade-off between makespan ratio Pareto optimality produced. An external archive utilized store non-dominated solutions generated from optimization process. Also, another improved version (MIMPA) by using Cauchy distribution instead Gaussian levy Flight increase algorithm’s convergence avoiding stuck into local minima possible investigated manuscript. The experimental outcomes proved superiority MIMPA over standard one under various performance metrics. However, couldnŠt overcome MHMPA even after strategy version. Furthermore, several well-known robust algorithms are used test efficacy method. experiment show that could achieve better employed metrics: Flow time, improvement percentage 414, 27257.46, 64151, 2 those metrics, respectively, compared second-best algorithm.