A Multi-objective Model for Task Scheduling Optimization in Fog-Cloud Computing Environments

With the advent of Internet of Things (IoT) applications, the number of processing requests has dramatically increased. In order to response to these requests, the Fog-Cloud environment has recently been introduced as a hybrid computing system. Although, the Fog-Cloud is a very promising environment for processing IoT requests, it faces many challenges. In this regard, task scheduling problem is one of the key challenges which has a significant impact on the efficiency and overall system cost. Motivated by this, in this paper, we first present a multi-objective optimization model including makespan, energy consumption and processing cost for scheduling tasks in an integrated Fog-Cloud environment. Then we propose a heuristic algorithm to efficiently solve the model. Simulation results demonstrate that our proposed algorithm significantly reduces all the aforementioned metrics and can achieve a good tradeoff between them. Specifically, the proposed algorithm improves the objective function around 98%, 43% and 32% in comparison with the random, genetic and the power of two choices algorithms, respectively.