MATEC Web Conf.
Volume 355, 20222021 International Conference on Physics, Computing and Mathematical (ICPCM2021)
|Number of page(s)||10|
|Section||Mathematical Science and Application|
|Published online||12 January 2022|
NTAM-LSTM models of network traffic prediction
1 School of Communication and Information Engineering, XI’an University of Post & Telecommunications, China
2 School of Electronic and Information Engineering, Xi’an Jiaotong University, China
* Corresponding author: firstname.lastname@example.org
Accurate prediction of network traffic is very important in allocating network resources. With the rapid development of network technology, network traffic becomes more complex and diverse. The traditional network traffic prediction model cannot accurately predict the current network traffic within the effective time. This paper proposes a Network Traffic Prediction Model----NTAM-LSTM, which based on Attention Mechanism with Long and Short Time Memory. Firstly, the model preprocesses the historical dataset of network traffic with multiple characteristics. Then the LSTM network is used to make initial prediction for the processed dataset. Finally, attention mechanism is introduced to get more accurate prediction results. Compared with other network traffic prediction models, NTAM-LSTM prediction model can achieve higher prediction accuracy and take shorter running time.
Key words: Network traffic prediction / LSTM neural network / Attention mechanism
© The Authors, published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.