Directed Search: A New Operator in NSGA-II for Task Scheduling in IoT Based on Cloud-Fog Computing

Abstract

In recent years, the Internet of Things (IoT) developments have made it one of the most important technologies. The exponential growth of data and increasing the number of latency-sensitive applications has necessitated a new approach to support these applications. The emerging fog computing architecture has partially addressed the issue of latency and other limitations of the IoT-based cloud computing paradigm. In order to achieve high-quality services and high system performance, an appropriate and efficient task scheduling method is needed, in addition, the energy consumption of computing devices should be considered. In this paper, a constraint bi-objective optimization problem is designed to minimize the servers’ energy consumption and overall response time simultaneously. Then, to solve this problem, by introducing a recombination operator and modifying NSGA-II, a directed non-dominated sorting genetic algorithm, called D-NSGA-II is proposed. This algorithm can control the selection pressure of agents, and balance the exploration and exploitation abilities of the algorithm using this new operator. To evaluate the performance of this algorithm, it is compared with well-known meta-heuristic algorithms. The experimental results demonstrate the D-NSGA-II has better performance than other algorithms. It can also respond to all requests before their deadline.