On the use of composite indicators for mobile communications network management in smart sustainable cities

Beyond 5G networks will be fundamental towards enabling sustainable mobile communication networks. One of the most challenging scenarios will be met in ultra-dense networks that are deployed in densely populated areas. In this particular case, mobile network operators should benefit from new assessment metrics and data science tools to ensure an effective management of their networks. In fact, incorporating architectures allowing a cognitive network management framework could simplify processes and enhance the network's performance. In this paper, we propose the use of composite indicators based on key performance indicators both as a tool for a cognitive management of mobile communications networks, as well as a metric which could successfully integrate more advanced user-centric measurements. Composite indicators can successfully synthesize and integrate large amounts of data, incorporating in a single index different metrics selected as triggers for autonomous decisions. The paper motivates and describes the use of this methodology, which is applied successfully in other areas with the aim of ranking metrics to simplify complex realities. A use case that is based on a universal mobile telecommunications system network is analyzed, due to technology simplicity and scalability, as well as the availability of key performance indicators. The use case focuses on analyzing the fairness of a network over different coverage areas as a fundamental metric in the operation and management of the networks. To this end, several ranking and visualization strategies are presented, providing examples of how to extract insights from the proposed composite indicator

Enabling multi-segment 5G service provisioning and maintenance through network slicing

This is a post-peer-review, pre-copyedit version of an article published in Journal of Network and Systems Management . The final authenticated version is available online at: http://dx.doi.org/10.1007/s10922-019-09509-9The current deployment of 5G networks in a way to support the highly demanding service types defined for 5G, has brought the need for using new techniques to accommodate legacy networks to such requirements. Network Slicing in turn, enables sharing the same underlying physical infrastructure among services with different requirements, thus providing a level of isolation between them to guarantee their proper functionality. In this work, we analyse from an architectural point of view, the required coordination for the provisioning of 5G services over multiple network segments/domains by means of network slicing, considering as well the use of sensors and actuators to maintain slices performance during its lifetime. We set up an experimental multi-segment testbed to demonstrate end-to-end service provisioning and its guarantee in terms of specific QoS parameters, such as latency, throughput and Virtual Network Function (VNF) CPU/RAM consumption. The results provided, demonstrate the workflow between different network components to coordinate the deployment of slices, besides providing a set of examples for slice maintenance through service monitoring and the use of policy-based actuations.Peer ReviewedPostprint (author's final draft

Diseño e implementación de una fuente de datos tipo SNORT basada en la arquitectura SELFNET

Este trabajo está basado en el proyecto SELFNET y cuenta con el apoyo del Programa Horizonte 2020 de la Comisión Europea con número de referencia H2020-ICT-2014-2/671672. El proyecto SELFNET diseña e implementa un marco de gestión autónomo de la red para lograr capacidades de auto-organización en la gestión de infraestructuras de red. Mediante la detección y mitigación automática de problemas de red, los operadores pueden reducir significativamente los costes operacionales. SELFNET integra en su arquitectura tecnologías innovadoras tales como las Redes Definidas por Software (SDN), la Virtualización de Funciones de Red (NFV), las Redes Auto-Organizadas (SON), Computación en la Nube, Inteligencia Artificial, Calidad de Experiencia (QoE), etc., para proporcionar un nuevo marco inteligente en la gestión de redes. El presente trabajo forma parte de la arquitectura de monitorización de SELFNET, diseñando e implementando una fuente de datos capaz de recibir notificaciones de un sensor tipo SNORT. Asimismo, la información recibida es adaptada según un modelo general de métricas. Finalmente, las métricas son enviadas a capas superiores de agregación y correlación. Los resultados obtenidos demuestran la eficiencia del diseño realizado