Dynamic core VNT adaptability based on predictive metro-flow traffic models
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Rights accessOpen Access
European Commission's projectMETRO-HAUL - METRO High bandwidth, 5G Application-aware optical network, with edge storage, compUte and low Latency (EC-H2020-761727)
MPLS-over-optical virtual network topologies (VNTs) can be adapted to near-future traffic matrices based on predictive models that are estimated by applying data analytics on monitored origin-destination (OD) traffic. However, the deployment of independent SDN controllers for core and metro segments can bring large inefficiencies to this core network reconfiguration based on traffic prediction when traffic flows from metro areas are rerouted to different ingress nodes in the core. In such cases, OD traffic patterns in the core might severely change, thus affecting the quality of the predictive OD models. New traffic model re-estimation usually takes a long time, during which no predictive capabilities are available for the network operator. To alleviate this problem, we propose to extend data analytics to metro networks to obtain predictive models for the metro flows; by knowing how these flows are aggregated into OD pairs in the core, we can also aggregate their predictive models, thus accurately predicting OD traffic and therefore enabling core VNT reconfiguration. To obtain quality metro-flow models, we propose an estimation algorithmthat processes monitored data and returns a predictive model. In addition, a flow controller is proposed for the control architecture to allow metro and core controllers to exchange metro-flow model information. The proposed model aggregation is evaluated through exhaustive simulation, and eventually experimentally assessed together with the flow controller in a testbed connecting premises in CNIT (Pisa, Italy) and UPC (Barcelona, Spain).
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CitationMorales, F., Gifre, L., Paolucci, F., Ruiz, M., Cugini, F., Castoldi, P., Velasco, L. Dynamic core VNT adaptability based on predictive metro-flow traffic models. "Journal of optical communications and networking", 1 Desembre 2017, vol. 9, núm. 12, p. 1202-1211.
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