Multi-objective Optimization of Resource Scheduling in Fog Computing Using an Improved NSGA-II

In conventional cloud computing technology, cloud resources are provided centrally by large data centers. For the exponential growth of cloud users, some applications, such as health monitoring and emergency response with the requirements of realtime and low-latency, cannot achieve efficient resource support. Therefore, fog computing technology has been proposed, where cloud services can be extended to the edge of the network to decrease the network congestion. In fog computing, the idle resources within many distributed devices can be used for providing services. An effective resource scheduling scheme is important to realize a reasonable management for these heterogeneous resources. Therefore, in this paper, a two-level resource scheduling model is proposed. In addition, we design a resource scheduling scheme among fog nodes in the same fog cluster based on the theory of the improved non-dominated sorting genetic algorithm II (NSGA-II), which considers the diversity of different devices. MATLAB simulation results show that our scheme can reduce the service latency and improve the stability of the task execution effectively.