基于长短期记忆网络的轮询系统性能预测

Performance prediction of polling system based on Long Short-Term Memory network

为解决轮询系统性能参数计算复杂的问题,提出一种基于长短期记忆(Long Short-Term Memory,LSTM)网络的轮询系统性能预测方法.通过对已有实验数据进行建模和分析,构建LSTM模型,预测不同信息分组到达率下的轮询系统性能.首先对实验数据进行处理,以不同信息分组到达率下的平均排队队长构成一个序列;然后建立包括输入层、隐藏层、全连接层和输出层的LSTM网络来执行预测;最后采用循环将网络的输出重新输入,以此来预测未知到达率下的平均排队队长.实验结果显示预测值曲线与真实值曲线表现出了相同的趋势,表明该方法能够有效预测不同轮询系统的性能.与传统的数学分析方法相比,该方法计算效率较高.

To solve the problem of complex calculation of polling system performance parameters,a performance prediction method of polling system based on Long Short-Term Memory(LSTM) network was proposed.By modeling and analyzing the existing experimental data,a LSTM model was constructed to predict the polling system performance under different information packets arrival rates.First,the experimental data were processed,and the average queue length under different information packets arrival rates was formed into a sequence.Then the LSTM network including input layer,hidden layer,fully connected layer and output layer was established to perform the prediction.Finally,the output of the network was re-input to predict the average queue length under unknown arrival rate.Through experiments,the predicted value and the real value curve show the same trend,which shows that this method can effectively predict the performance of the polling systems.Compared with the traditional mathematical analysis method,this method is more efficient.