Deep Learning Based Traffic Prediction Method for Digital Twin Network

Junyu Lai; Zhiyong Chen; Junhong Zhu; Wanyi Ma; Lianqiang Gan; Siyu Xie; Gun Li

doi:10.1007/s12559-023-10136-5

Deep Learning Based Traffic Prediction Method for Digital Twin Network

Cognit Comput. 2023 May 27:1-19. doi: 10.1007/s12559-023-10136-5. Online ahead of print.

Authors

Junyu Lai¹, Zhiyong Chen¹, Junhong Zhu¹, Wanyi Ma¹, Lianqiang Gan¹, Siyu Xie¹, Gun Li¹

Affiliation

¹ School of Aeronautics & Astronautics, University of Electronic Science and Technology of China, Chengdu, 611731 China.

Abstract

Network traffic prediction (NTP) can predict future traffic leveraging historical data, which serves as proactive methods for network resource planning, allocation, and management. Besides, NTP can also be applied for load generation in simulated and emulated as well as digital twin networks (DTNs). This paper focuses on accurately predicting background traffic matrix (TM) of typical local area network (LAN) for traffic synchronization in DTN. A survey is firstly conducted on DTN, conventional model, and deep learning based NTP methods. Then, as the major contribution, a linear feature enhanced convolutional long short-term memory (ConvLSTM) model based NTP method is proposed for LAN. An autoregressive unit is integrated into the ConvLSTM model to improve its linear prediction ability. In addition, this paper further optimizes the proposed model from both spatial and channel-wise dimensions. Particularly, a traffic pattern attention (TPA) block and a squeeze & excitation (SE) block are derived and added to the enhanced ConvLSTM (eConvLSTM) model. Comparative experiments demonstrate that the eConvLSTM model outperforms all the baselines. It can improve the prediction accuracy by reducing the mean square error (MSE) up to 10.6% for one-hop prediction and 16.8% for multi-hops prediction, compared to the legacy CovnLSTM model, with still satisfying the efficiency requirements. The further enhancement of the eConvLSTM model can additionally reduce the MSE about 2.1% for one-hop prediction and 4.2% for multi-hops prediction, with slightly degrading efficiency. The proposed eConvLSTM model based NTP method can play a vital role on DTN traffic synchronization.

Keywords: Deep neural network; Digital twin network; LSTM; Traffic matrix; Traffic prediction.

© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.