Spatial-temporal graph attention networks for dynamic traffic prediction of SDN

Guanghong Lyu; Hengjie Zheng

doi:10.1117/12.3035035

7 August 2024 Spatial-temporal graph attention networks for dynamic traffic prediction of SDN

Guanghong Lyu, Hengjie Zheng

Proceedings Volume 13224, 4th International Conference on Internet of Things and Smart City (IoTSC 2024); 132240W (2024) https://doi.org/10.1117/12.3035035
Event: 4th International Conference on Internet of Things and Smart City, 2024, Hangzhou, China

Abstract

For the problem of static space and time dependencies based on traffic prediction in SDN traffic engineering, this paper proposes a dynamic network traffic prediction method, Attention mechanism for GCNGRU model (AGCNGRU), which integrates graph convolutional neural networks (GCN) with gated recurrent units (GRU) and incorporates an attention mechanism. By leveraging GCN, it captures the spatial dependency of traffic between nodes in the network, while GRU captures the temporal dependency of traffic passing through various nodes. The time attention mechanism is designed to assign weights to each hidden state, adjusting the importance of traffic information at different time points. Simultaneously, a data-driven spatial attention mechanism dynamically and adaptively adjusts the Laplace matrix, enabling dynamic extraction of spatial-temporal correlation in traffic data. This ultimately leads to accurate prediction of dynamic traffic. Experimental results on the GEANT datasets demonstrate that the proposed method significantly outperforms other approaches.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Guanghong Lyu and Hengjie Zheng "Spatial-temporal graph attention networks for dynamic traffic prediction of SDN", Proc. SPIE 13224, 4th International Conference on Internet of Things and Smart City (IoTSC 2024), 132240W (7 August 2024); https://doi.org/10.1117/12.3035035

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