An Unsupervised Approach to Infer Quality of Service for Large-Scale Wireless Networking
| Autor: | Dianne S. V. Medeiros, Luiz Magalhaes, Diogo M. F. Mattos, Lucio H. A. Reis |
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| Rok vydání: | 2020 |
| Předmět: |
Service (systems architecture)
Computer Networks and Communications business.industry Computer science Wireless network Strategy and Management Quality of service 020206 networking & telecommunications 02 engineering and technology Network monitoring computer.software_genre Usage data Hardware and Architecture 0202 electrical engineering electronic engineering information engineering Unsupervised learning Wireless 020201 artificial intelligence & image processing Data mining Cluster analysis business computer Information Systems |
| Zdroj: | Journal of Network and Systems Management. 28:1228-1247 |
| ISSN: | 1573-7705 1064-7570 |
| DOI: | 10.1007/s10922-020-09530-3 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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