Task Scheduling and Resource Balancing of Fog Computing in Smart Factory

With the development of new generation information technology, many traditional factories begin to transform to smart factories. How to process the huge volume data in the smart factories so as to improve the production efficiency is still a serious problem. Based on the characteristics of smart factory, a fog computing framework suitable for smart factory is proposed, and Kubernetes is used to automatically deploy containerized smart factory applications. First, in the scene of fog computing, an improved interval division genetic scheduling algorithm IDGSA (Interval Division Genetic Scheduling Algorithm) based on genetic algorithm is proposed to schedule and allocate tasks in smart factory. We consider the optimization of task execution time and resource balance at same time and combined with IDGSA, the optimized scheduling decision is given. Second, we further design an architecture of cloud and fog collaborative computing. In this scenario, we propose the IDGSA-P (Interval Division Genetic Scheduling Algorithm with Penalty factor) for optimization based on IDGSA. Finally, we carry out simulation experiments to verify the performance of the proposed algorithms. The simulation results show that compared with Kubernetes default scheduling algorithm, IDGSA can reduce data processing time by 50% and improve the utilization of fog computing resources by 60%. Compared with traditional genetic algorithm, with fewer iterations, IDGSA can reduce data processing time by 7% and improve the utilization of fog computing resources by 9%. And compared with the conventional Joines&Houck method, the proposed IDGSA-P algorithm can converge much faster and archived better optimization results. Further, the simulation shows that IDGSA-P in cloud and fog collaborative computing can reduce the total task delay by 18% and 7%, respectively, when compare to only-cloud and only-fog computing.