BiLSTM-based selection and prediction of optimal polling systems for multiple server numbers

Facing the demands of different service scenarios and the large number of base stations deployed in the context of the 5G era, network slicing was introduced to support on-demand services for specific service scenarios. The base stations in this context, are utilized to provide services within different slices in each service scenario. Due to the complexity of selection of the most appropriate base stations, a multi-server polling system for 5G network slicing is proposed in this work, with a method of predicting the optimal number of base stations to be selected. In order of solve the problem of difficult mathematical analysis, low service efficiency, high delay and waste of resources due to unlimited number of base stations in the network, the selection method makes use of a Bi-directional Long Short Term Memory (BiLSTM) neural network. First, the experiment was scaled up and multidimensional variables were varied to obtain data on the average queue length and average delay of the system; Next, a neural network model is constructed for performance prediction; Finally, the optimal number of base stations is selected and a posture prediction is made for that number of base stations. The experimental results show that the system performance is best when the number of base stations is 3, which saves resources and reduces the difficulty of mathematical derivation and analysis.