Optical Communication Technology, Volume. 48, Issue 6, 34(2024)

SDN traffic prediction model based on adaptive spatiotemporal network

LIU Yue1, ZHANG Hui2, CAI Anliang1, and SHEN Jianhua1
Author Affiliations
  • 1School of Communication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
  • 2CypressTel SHENZHEN Communication Technology Company, Shenzhen Guangdong 518000, China
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    References(20)

    [1] [1] VINAYAKUMAR R, SOMAN K P, POORNACHANDRAN P. Applying deep learning approaches for network traffic prediction [C]//IEEE. Proceedings of 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI). Manipal: IEEE, 2017: 2353-2358.

    [2] [2] TUNE P, ROUGHAN M, HADDADI H, et al. Internet traffic matrices: A primer[J]. Recent Advances in Networking, 2013, 1: 1-56.

    [3] [3] KREUTZ D, RAMOS F M V, VERISSIMO P E, et al. Software-defined networking: a comprehensive survey [J]. Proceedings of the IEEE, 2014, 103(1): 14-76.

    [4] [4] MOAYEDI H Z, MASNADI-SHIRAZI M A. Arima model for network traffic prediction and anomaly detection[C]//IEEE. Proceedings of 2008 international symposium on information technology. Kuala Lumpur: IEEE, 2008, 4: 1-6.

    [5] [5] STOCK J H, WATSON M W. Vector autoregressions[J]. Journal of Economic perspectives, 2001, 15(4): 101-115.

    [6] [6] BERMOLEN P, ROSSI D. Support vector regression for link load prediction[J]. Computer Networks, 2009, 53(2): 191-201.

    [7] [7] NIE L, JIANG D, GUO L, et al. Traffic matrix prediction and estimation based on deep learning for data center networks [C]//IEEE. Proceedings of 2016 IEEE Globecom Workshops (GC Wkshps). Washington: IEEE, 2016: 1-6.

    [8] [8] ZAREMBA W, SUTSKEVER I, VINYALS O. Recurrent neural network regularization[EB/OL]. (2014-09-08) [2024-01-19]. https://www. semanticscholar.org/paper/Recurrent-Neural-Network-Regularization-Zaremba-Sutskever/f264e8b33c0d49a692a6ce2c4bcb28588aeb7d97.

    [9] [9] GRAVES A. Supervised sequence labelling with recurrent neural networks. studies in computational intelligence[M]. Berlin: Springer, 2012.

    [10] [10] AZZOUNI A, PUJOLLE G. NeuTM: a neural network-based framework for traffic matrix prediction in SDN[C]//IEEE. Proceedings of NOMS 2018-2018 IEEE/IFIP Network Operations and Management Symposium. Taipei: IEEE, 2018: 1-5.

    [11] [11] CHUNG J, GULCEHRE C, CHO K H, et al. Empirical evaluation of gated recurrent neural networks on sequence modeling [EB/OL]. (2014 -12-11) [2024-01-19]. https://arxiv.org/abs/1412.3555.

    [12] [12] LE D H, TRAN H A, SOUIHI S, et al. An ai-based traffic matrix prediction solution for software-defined network [C]//IEEE. Proceedings of ICC 2021-IEEE International Conference on Communications. Virtual/Mon treal: IEEE, 2021: 1-6.

    [13] [13] RAMAKRISHNAN N, SONI T. Network traffic prediction using recurrent neural networks [C]//IEEE. Proceedings of 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA). Orlando: IEEE, 2018: 187-193.

    [14] [14] KIPF T N, WELLING M. Semi-supervised classification with graph convolutional networks [EB/OL]. [2024-01-19]. https://xueshu.baidu.com/ usercenter/paper/show?paperid=4bbac8399145c63d7eb85f702ad5f111&site=xueshu_se.

    [15] [15] WU Z, PAN S, CHEN F, et al. A comprehensive survey on graph neural networks [J]. IEEE transactions on neural networks and learning systems, 2020, 32(1): 4-24.

    [17] [17] ZHAO L, SONG Y, ZHANG C, et al. T-gcn: a temporal graph convolutional network for traffic prediction [J]. IEEE transactions on intelligent transportation systems, 2019, 21(9): 3848-3858.

    [18] [18] ZHU J, WANG Q, TAO C, et al. AST-GCN: attribute-augmented spatiotemporal graph convolutional network for traffic forecasting [J]. IEEE Access, 2021, 9: 35973-35983.

    [19] [19] GUI Y, WANG D, GUAN L, et al. Optical network traffic prediction based on graph convolutional neural networks [C]// IEEE. Proceedings of 2020 Opto-Electronics and Communications Conference (OECC). Taipei: IEEE, 2020: 1-3.

    [20] [20] LAI G, CHANG W C, YANG Y, et al. Modeling long-and short-term temporal patterns with deep neural networks [C]//ACM. Proceedings of The 41st international ACM SIGIR conference on research & development in information retrieval. Ann Arbor: ACM, 2018: 95-104.

    [21] [21] UHLIG S, QUOITIN B, LEPROPRE J, et al. Providing public intrado-main traffic matrices to the research community[J]. ACM SIGCOMM Computer Communication Review, 2006, 36(1): 83-86.

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    LIU Yue, ZHANG Hui, CAI Anliang, SHEN Jianhua. SDN traffic prediction model based on adaptive spatiotemporal network[J]. Optical Communication Technology, 2024, 48(6): 34

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    Paper Information

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    Received: Jan. 19, 2024

    Accepted: Jan. 16, 2025

    Published Online: Jan. 16, 2025

    The Author Email:

    DOI:10.13921/j.cnki.issn1002-5561.2024.06.007

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