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Impact of Traffic Sampling on LRD Estimation

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Information Systems and Technologies (WorldCIST 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 799))

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Abstract

Network traffic sampling is an effective method for understanding the behavior and dynamics of a network, being essential to assist network planning and management. Tasks such as controlling Service Level Agreements or Quality of Service, as well as planning the capacity and the safety of a network can benefit from traffic sampling advantages.

The main objective of this paper is focused on evaluating the impact of sampling network traffic on: (i) achieving a low-overhead estimation of the network state and (ii) assessing the statistical properties that sampled network traffic presents regarding the eventual persistence of Long-Range Dependence (LRD). For that, different Hurst parameter estimators have been used. Facing the impact of LRD on network congestion and traffic engineering, this work will help clarify the suitability of distinct sampling techniques in accurate network analysis.

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Notes

  1. 1.

    Available at: https://publicdata.caida.org/datasets/passive/.

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Acknowledgements

This work is financed by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciôncia e a Tecnologia, within project LA/P/0063/2020, and by FCT, within the R &D Units Project Scope: UIDB/00319/2020.

Author information

Authors and Affiliations

  1. Centro Algoritmi, Universidade do Minho, Braga, Portugal

    João Mendes, Solange Rito Lima & Paulo Carvalho

  2. INESC TEC, Universidade do Minho, Braga, Portugal

    João Marco C. Silva

Authors
  1. João Mendes
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  2. Solange Rito Lima
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  3. Paulo Carvalho
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  4. João Marco C. Silva
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Corresponding author

Correspondence to João Marco C. Silva .

Editor information

Editors and Affiliations

  1. ISEG, Universidade de Lisboa, Lisbon, Cávado, Portugal

    Alvaro Rocha

  2. College of Engineering, The Ohio State University, Columbus, OH, USA

    Hojjat Adeli

  3. Institute of Data Science and Digital Technologies, Vilnius University, Vilnius, Lithuania

    Gintautas Dzemyda

  4. DCT, Universidade Portucalense, Porto, Portugal

    Fernando Moreira

  5. TeCIP Institute, Scuola Superiore Sant’Anna, Pisa, Italy

    Valentina Colla

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Mendes, J., Lima, S.R., Carvalho, P., Silva, J.M.C. (2024). Impact of Traffic Sampling on LRD Estimation. In: Rocha, A., Adeli, H., Dzemyda, G., Moreira, F., Colla, V. (eds) Information Systems and Technologies. WorldCIST 2023. Lecture Notes in Networks and Systems, vol 799. Springer, Cham. https://doi.org/10.1007/978-3-031-45642-8_3

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