Moving edge computing scheduling algorithm based on incentive mechanism

In the industrial production process, due to limitations such as hardware configuration, production environment, network latency, and heterogeneous devices, there are a large number of edge resources in cloud manufacturing networks, which require timely local self-organizing intelligent operation without relying on the cloud. To improve this situation, a self-organizing intelligent algorithm based on component resource services is proposed. This algorithm adopts evolutionary game theory, with a simple computational model and lightweight algorithm. Game decisions can be deployed to edge devices without adjusting parameters. The game model directly analyzes the economic benefits of the entire manufacturing process. The evolutionary dynamic model in evolutionary game theory is used to analyze the cooperative and non-cooperative behavior of edge resources. By encapsulating the evolutionary game algorithm in a component-based manner, the algorithm achieves maximum game benefits between resources and within resources with limited computing power. Simulation shows that the reusability and real-time performance of this algorithm are superior to traditional algorithms.