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Forecasting short-term data center network traffic load with convolutional neural networks

Author

Listed:
  • Alberto Mozo
  • Bruno Ordozgoiti
  • Sandra Gómez-Canaval

Abstract

Efficient resource management in data centers is of central importance to content service providers as 90 percent of the network traffic is expected to go through them in the coming years. In this context we propose the use of convolutional neural networks (CNNs) to forecast short-term changes in the amount of traffic crossing a data center network. This value is an indicator of virtual machine activity and can be utilized to shape the data center infrastructure accordingly. The behaviour of network traffic at the seconds scale is highly chaotic and therefore traditional time-series-analysis approaches such as ARIMA fail to obtain accurate forecasts. We show that our convolutional neural network approach can exploit the non-linear regularities of network traffic, providing significant improvements with respect to the mean absolute and standard deviation of the data, and outperforming ARIMA by an increasingly significant margin as the forecasting granularity is above the 16-second resolution. In order to increase the accuracy of the forecasting model, we exploit the architecture of the CNNs using multiresolution input distributed among separate channels of the first convolutional layer. We validate our approach with an extensive set of experiments using a data set collected at the core network of an Internet Service Provider over a period of 5 months, totalling 70 days of traffic at the one-second resolution.

Suggested Citation

  • Alberto Mozo & Bruno Ordozgoiti & Sandra Gómez-Canaval, 2018. "Forecasting short-term data center network traffic load with convolutional neural networks," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-31, February.
  • Handle: RePEc:plo:pone00:0191939
    DOI: 10.1371/journal.pone.0191939
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    References listed on IDEAS

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    1. Tan, Zhongfu & Zhang, Jinliang & Wang, Jianhui & Xu, Jun, 2010. "Day-ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models," Applied Energy, Elsevier, vol. 87(11), pages 3606-3610, November.
    2. M. Piacentini & F. Rinaldi, 2011. "Path loss prediction in urban environment using learning machines and dimensionality reduction techniques," Computational Management Science, Springer, vol. 8(4), pages 371-385, November.
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    1. Kambombo Mtonga & Santhi Kumaran & Chomora Mikeka & Kayalvizhi Jayavel & Jimmy Nsenga, 2019. "Machine Learning-Based Patient Load Prediction and IoT Integrated Intelligent Patient Transfer Systems," Future Internet, MDPI, vol. 11(11), pages 1-24, November.
    2. Xingsheng Shu & Wei Ding & Yong Peng & Ziru Wang & Jian Wu & Min Li, 2021. "Monthly Streamflow Forecasting Using Convolutional Neural Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(15), pages 5089-5104, December.
    3. Tian, Zhongda, 2020. "Chaotic characteristic analysis of network traffic time series at different time scales," Chaos, Solitons & Fractals, Elsevier, vol. 130(C).
    4. Chenhua Ni & Xiandong Ma, 2018. "Prediction of Wave Power Generation Using a Convolutional Neural Network with Multiple Inputs," Energies, MDPI, vol. 11(8), pages 1-18, August.
    5. Shuoben Bi & Cong Yuan & Shaoli Liu & Luye Wang & Lili Zhang, 2022. "Spatiotemporal Prediction of Urban Online Car-Hailing Travel Demand Based on Transformer Network," Sustainability, MDPI, vol. 14(20), pages 1-21, October.

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