A Study of Deep Learning for Network Traffic Data Forecasting

@article{Pflb2019ASO,
  title={A Study of Deep Learning for Network Traffic Data Forecasting},
  author={Benedikt Pf{\"u}lb and Christoph Hardegen and Alexander Gepperth and Sebastian Rieger},
  journal={ArXiv},
  year={2019},
  volume={abs/1909.04501}
}
  • Benedikt Pfülb, Christoph Hardegen, +1 author Sebastian Rieger
  • Published 2019
  • Mathematics, Computer Science
  • ArXiv
  • We present a study of deep learning applied to the domain of network traffic data forecasting. This is a very important ingredient for network traffic engineering, e.g., intelligent routing, which can optimize network performance, especially in large networks. In a nutshell, we wish to predict, in advance, the bit rate for a transmission, based on low-dimensional connection metadata (“flows”) that is available whenever a communication is initiated. Our study has several genuinely new points… CONTINUE READING

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    References

    Publications referenced by this paper.
    SHOWING 1-9 OF 9 REFERENCES

    Online flow size prediction for improved network routing

    VIEW 8 EXCERPTS
    HIGHLY INFLUENTIAL

    A survey of techniques for internet traffic classification using machine learning

    VIEW 3 EXCERPTS
    HIGHLY INFLUENTIAL

    Learning to Route

    VIEW 1 EXCERPT

    A Framework for QoS-aware Traffic Classification Using Semi-supervised Machine Learning in SDNs

    VIEW 1 EXCERPT

    An efficient elephant flow detection with cost-sensitive in SDN

    VIEW 1 EXCERPT