Load Modeling and Task Allocation Strategy of Concurrent Business Computing in Power Internet of Things Based on Kernel Density Estimation
In the context of the Power Internet of Things,a large number of concurrent services pose a challenge to the processing capacity of edge computing terminals.Therefore,a computing load modelling and task allocation strategy for concurrent services in the Power Internet of Things based on kernel density estimation was proposed.Based on the kernel density estimation theory,a model of concurrent service coverage level and computing load was established.According to the service coverage level,the resource allocation of edge computing terminals was determined,and the task allocation of cloud side collaboration was determined with the goal of minimizing processing delay.The orderly charging service of electric vehicles was taken as an example for simulation analysis.The results show that the proposed model and method can improve the overall efficiency of the system computing resources,reduce the business delay,and improve the ability of the Power Internet of Things to deal with concurrent business processing requirements.
Power Internet of Thingsnuclear density estimationcalculated loadtask assignmentorderly charging
万方数据
维普
