Data analytics based origin-destination core traffic modelling
Document typeConference report
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Rights accessRestricted access - publisher's policy
Traffic monitoring is an essential task for network operators since it allows evaluating network performance. Monitoring data from origin-destination (OD) traffic in core virtual network topologies can be collected from packet nodes and stored in a repository for further analysis, e.g., to detect anomalies or to create predicted traffic matrices for the near future. In this paper we propose a set of modules to support data analytics-based algorithms along with a machine learning procedure based on artificial neural networks (ANN) that provides robust and adaptive traffic models.
CitationMorales, F., Ruiz, M., Velasco, L. Data analytics based origin-destination core traffic modelling. A: International Conference on Transparent Optical Networks. "ICTON 2017: 19th International Conference on Transparent Optical Networks: Girona, Catalonia, Spain, 2-6 July 2017". Girona: Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 1-4.