Analysis of distributed database access path prediction based on recurrent neural network in internet of things. (17th January 2019) - Yu, Guangzhou; Fu, Weina
Summary: For avoiding the phenomenon of congestion, delay, and jitter in thedatabase access path, it is necessary to study the prediction method of database access path. Predicted database access path has high delay and jitter when using existing database access path prediction method to predict the path. Therefore, a prediction method of database access path is proposed based on the recurrent neural network. A decision matrix is constructed and normalized based on the analytic hierarchy process. The evaluation value of the alternative transit data center is calculated by the arithmetic weighted average operator, and the transit data center is selected according to the evaluation result. The matter‐element analysis model is used to establish the mapping relationship between the user experience quality and the network service quality parameters. Moreover, the user experience quality evaluation level objective function of the database access path prediction method is constructed. Through the objective function to obtain the optimal database access path, the database access path prediction is completed. The experimental results show that the delay of the database access path predicted by the proposed method is much lower than other methods. The jitter is less than 30 ms and the jitter is small, which verifies the effectiveness of the path prediction method.