A Mobility Aware Task Offloading Scheme Based On Ant Colony Optimization Algorithm In Software-Defined Fog Computing

Fog computing is a new paradigm which enables offloading IOT data (tasks) to the network devices. The aim of this approach is reducing the response time for delay-sensitive applications and improving the quality of service for users. This paper presents a task offloading scheme with taking advantages of the Software-Defind Networks. In this research a mixed integer linear programming (MILP) optimization model is presented with the aim of minimizing delay and mobility cost of things, which considers local computing, fog nodes participation, applications distribution and resource limitations. Whereas the presented mathematical model is Np-hard, a meta-heuristic algorithm based on the ant colony optimization is proposed by considering constraints of the mathematical model. The results obtained from evaluation of the proposed method is compared with the optimal value obtained from the mathematical model, random method and a heuristic algorithm presented in related works. The results of evaluation show that the delay and the total offloading cost in the proposed method are 22% and 28.75% higher than the optimal values, respectively. Also, the proposed method is capable to reduce the delay by 20% and reduce the migration cost of computing results by 40% compared to the heuristic method in state-of-the-art.