Abstract
Accurate prediction of website traffic can improve network management, improve service quality, and improve the end user experience. Using the neural network learning and memory function, we can predict the time series of network traffic flow. Based on short - and long-term memory, we design the structure of data and neural network model and select the nonlinear activation function. The experimental results show that the proposed prediction method obtains the higher accuracy, which can effectively predict the traffic of visiting websites. At the same time, this method can effectively reduce the training time. By accurate traffic prediction, the network manager can adjust scheduling strategy to guarantee the user experience.
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Acknowledgment
This work is partly supported by Jiangsu technology project of Housing and Urban-Rural Development (No. 2018ZD265) and Xu Zhou Science and Technology Plan Project (No. KC21309).
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Bao, R., Zhang, K., Huang, J., Li, Y., Liu, W., Wang, L. (2022). Research on Website Traffic Prediction Method Based on Deep Learning. In: Jiang, D., Song, H. (eds) Simulation Tools and Techniques. SIMUtools 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 424. Springer, Cham. https://doi.org/10.1007/978-3-030-97124-3_32
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