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Simulation Tools and Techniques. 13th EAI International Conference, SIMUtools 2021, Virtual Event, November 5-6, 2021, Proceedings

Research Article

Research on Website Traffic Prediction Method Based on Deep Learning

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  • @INPROCEEDINGS{10.1007/978-3-030-97124-3_32,
        author={Rong Bao and Kailiang Zhang and Jing Huang and Yuxin Li and Weiwei Liu and Likai Wang},
        title={Research on Website Traffic Prediction Method Based on Deep Learning},
        proceedings={Simulation Tools and Techniques. 13th EAI International Conference, SIMUtools 2021, Virtual Event, November 5-6, 2021, Proceedings},
        proceedings_a={SIMUTOOLS},
        year={2022},
        month={3},
        keywords={Network traffic prediction Bidirectional LSTM Deep learning Activation function Automatic polling},
        doi={10.1007/978-3-030-97124-3_32}
    }
    
  • Rong Bao
    Kailiang Zhang
    Jing Huang
    Yuxin Li
    Weiwei Liu
    Likai Wang
    Year: 2022
    Research on Website Traffic Prediction Method Based on Deep Learning
    SIMUTOOLS
    Springer
    DOI: 10.1007/978-3-030-97124-3_32
Rong Bao1, Kailiang Zhang1,*, Jing Huang1, Yuxin Li1, Weiwei Liu2, Likai Wang2
  • 1: Jiangsu Province Key Laboratory of Intelligent Industry Control Technology, Xuzhou University of Technology
  • 2: Traffic Police Detachment, Xuzhou Police Bureau
*Contact email: zhangkailiang@xzit.edu.cn

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.

Keywords
Network traffic prediction Bidirectional LSTM Deep learning Activation function Automatic polling
Published
2022-03-31
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-030-97124-3_32
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