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ARGCN: An intelligent prediction model for SDN network performance

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Abstract

Traditional methods for analyzing network performance have limitations, including high costs and over-simplified assumptions, which are not helpful for network administrators managing increasingly complex networks. Therefore, it is necessary to provide a performance prediction method specifically designed for complex networks. This paper introduces the Attention-based Recurrent Graph Convolutional Network (ARGCN), a tailored performance prediction model for Software-defined Networks (SDNs). SDNs extract network data dynamically, and ARGCN, using a Message Passing Neural Network (MPNN) framework, transmits and aggregates information, incorporating a recurrent neural network with an attention mechanism to handle complex dependencies among link nodes. Experimental validation demonstrates the model’s efficiency in forecasting network metrics with over 95% accuracy, even in worst-case scenarios. ARGCN, integrating MPNN, recurrent neural networks, and attention mechanisms, emerges as a powerful tool for administrators dealing with SDN intricacies.

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All of the material is owned by the authors. Some of the data used in this article can refer to the Section 4 of the article, and other data acquisition can contact the corresponding author of this article.

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Acknowledgements

This work was supported in part by the Natural Science Foundation of China (61871468, 62111540270), the Zhejiang Provincial Natural Science Foundation of China (LZ23F010003, LQ23F010009), Zhejiang Provincial Key Laboratory of New Network Standards and Technologies (NNST)(No.2013E10012), Zhejiang Gongshang University “Digital+” Disciplinary Construction Management Project (Project Number SZJ2022C010, SZJ2022B010), the Fundamental Research Funds for the Provincial Universities of Zhejiang [XRK22005].

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Authors and Affiliations

  1. School of Information and Electronic Engineering, Zhejiang Gongshang University, Xuezheng Street, Hangzhou, 310018, Zhejiang, China

    Bo Ma, Qin Lu, Xuxin Fang, Junhu Liao, Zebin Chen & Chuanhuang Li

  2. School of Information and Electrical Engineering (Sussex Artificial Intelligence Institute), Zhejiang Gongshang University, Xuezheng Street, Hangzhou, 310018, Zhejiang, China

    Haoyue Liu

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  1. Bo Ma
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Contributions

Bo Ma: Conceptualization, Methodology, Wri-ting- Reviewing and Editing, Supervision. Qin Lu: Methodology, Data curation, Investigation, Writing- Reviewing and Editing. Xuxin Fang and Junhu Liao: Conceptualization, Visualization, Writing- Reviewing and Editing Zebin Chen and Haoyue Liu: Investigation, Visualization, Writing- Reviewing and Editing. Chuanhuang Li: Conceptualization, Supervision, Writing- Reviewing and Editing.

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Correspondence to Chuanhuang Li.

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Ma, B., Lu, Q., Fang, X. et al. ARGCN: An intelligent prediction model for SDN network performance. Peer-to-Peer Netw. Appl. 17, 1422–1441 (2024). https://doi.org/10.1007/s12083-024-01656-4

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