Performance analysis and prediction of asymmetric two-level priority polling system based on BP neural network

Concerning the needs of multi-service and network performance prediction in the Internet of Things (IoT), we propose an asymmetric two-priority polling control system model, and use the neural network algorithm to predict and analyze its performance. Firstly, the mathematical model of the system in the continuous time state is established by using the embedded Markov chain theory and the probability generating function. Meanwhile, the characteristics like the average queue length and average cycle of the system are accurately analyzed, and verified in simulation experiments. Subsequently, a three-layer multi-input single-output backpropagation (BP) network model is constructed to predict the performance of the polling system. The results show that the model can not only distinguish multi-business tasks, but also ensure the system delay. BP neural network prediction algorithm can accurately predict the performance of the system, which has a guiding significance for its performance evaluation, and provides a new method for the research of the polling system. (C) 2020 Elsevier B.V. All rights reserved.