Energy Efficient Scheduling for Heterogeneous Fog Computing Architectures

Abstract

Heterogeneous fog computing architectures that hybridize different types of edge nodes can achieve better scalability and lower cost to serve massive number of Internet Of Things (IOT) devices than the centralized architecture of cloud computing. In this paper, we propose an efficient scheduling algorithm to minimize the energy consumption for IOT workflows on heterogeneous fog computing architectures. We first build an integer linear programming model that minimizes the total energy. The purpose of the ILP model is not to be used directly for the computation, but to reveal key factors to minimize the energy consumption in a distributed system. Based on the observations from the model, we derive an energy minimization scheduling (EMS) algorithm that combines different policies to achieve the near optimal scheduling. We implemented and tested our model and algorithm via simulations. The experimental results show that EMS can achieve near optimal energy consumption with much faster execution time.