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An Unsupervised Approach to Infer Quality of Service for Large-Scale Wireless Networking

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Abstract

Inferring the quality of service experienced by wireless users is challenging, as network monitoring does not capture the service perception for each user individually. In this paper, we propose an unsupervised machine learning approach to infer the quality of service experienced by wireless users, based on the different usage profiles of a large-scale wireless network. To this end, our approach correlates the usage data of access points, and the summaries of connection flows passing through the access points in the network. Then, we apply the k-means clustering algorithm to infer different network usage profiles. We evaluate our proposed approach to infer QoS on a real large-scale wireless network, and the results show that discriminating the flows into five clusters allows identifying prevalent usage profiles of the degraded state of the network and overload conditions in access points, considering only the flow summaries.

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Notes

  1. The collected data refers to the period between April 17 and 24, 2018.

  2. The day used for the test is March 27, 2018.

  3. Available at http://w1.fi/hostapd/.

  4. Available at https://www.rsyslog.com/.

  5. Available at https://tools.netsa.cert.org/silk/.

  6. The proposal considers the Euclidean distance as a metric to indicate the separation between clusters.

  7. Available at https://pandas.pydata.org/.

  8. Available at https://scikit-learn.org/stable/.

  9. Available at https://www.knime.com/.

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Funding

This research is supported by CNPq (437085/2018-0), CAPES, RNP, and FAPERJ.

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  1. MídiaCom - PPGEET/TCE/UFF, Rua Passo da Pátria, 156, Niterói, RJ, Brazil

    Lucio Henrik A. Reis, Luiz Claudio S. Magalhães, Dianne Scherly V. de Medeiros & Diogo M. F. Mattos

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  1. Lucio Henrik A. Reis
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  2. Luiz Claudio S. Magalhães
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  3. Dianne Scherly V. de Medeiros
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  4. Diogo M. F. Mattos
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Correspondence to Diogo M. F. Mattos.

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Reis, L.H.A., Magalhães, L.C.S., de Medeiros, D.S.V. et al. An Unsupervised Approach to Infer Quality of Service for Large-Scale Wireless Networking. J Netw Syst Manage 28, 1228–1247 (2020). https://doi.org/10.1007/s10922-020-09530-3

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  • DOI: https://doi.org/10.1007/s10922-020-09530-3

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