Abstract:The paper considers the trade-off between the impact of edge node computing resources on task analysis latency and accuracy, which is currently overlooked in research. Hence, by proposing a dynamic configuration strategy for allocating computing resources for video analysis tasks at edge nodes, the article tries to realize the best balance between tasks latency and accuracy through online resource allocation. The solution maximizes the objective function by optimizing computing resource objectives, i.e. maximizing accuracy while reducing latency. Due to the lack of data sets, the Error function is minimized by utilizing the simulation to fit the accuracy function. At the same time, the gradient estimation method is used to find the gradient descent direction of the objective function, and the minimum value is solved through iterations to solve the problem of not being able to directly obtain the gradient caused by the unpredictability of the neural network. The superiority of the algorithm is verified through simulations and control experiments, which can efficiently reduce network load and improve overall resource utilization and performance level. The paper also discusses situations of improving resource utilization with the edge-cloud collaboration architecture, and investigates the future research direction.