Conference paper Open Access
Albert Pagès; Fernando Agraz; Salvatore Spadaro; Rafael Montero; Xenofon Vasilakos; Nasim Ferdosian; Navid Nikaein; Dean Lorenz; Kenneth Nagin; Marouane Mechteri; Yosra Ben Slimen; Rui Pedro; Guilherme Cardoso; Pedro Neves; Nuno Henriques; Qi Wang; José M. Alcaraz Calero; Antonio Matencio Escolar; Pablo Salva; Enrique Chirivella Perez; Ricardo Marco Alaez
Provisioning of network slices with appropriate Quality of Experience (QoE) guarantees is one of the key enablers for 5G networks. However, it poses several challenges in the slice management that need to be addressed to achieve an efficient end-to-end (E2E) services delivery. These challenges, among others, include the estimation of QoE Key Performance Indicators (KPIs) from monitored metrics and the corresponding reconfiguration operations (actuations) in order to support and maintain the desired quality levels. In this context, SliceNet provides a design and an implementation of a cognitive slice management framework that leverages machine learning (ML) techniques in order to proactively maintain network conditions in the required state that assures E2E QoE, as perceived by the vertical customers.