A Scalable Telemetry Framework for Zero Touch Optical Network Management

The interest about Zero Touch Network and Service Management (ZSM) is rapidly emerging. As defined by ETSI, the ZSM architecture is based on a closed-loop/feedback control of the network and the services. Such closed-loop control can be based on the Boyd's Observe Orient Decide and Act (OODA) loop that matches some specific management functions such as Data Collection, Data Analytics, Intelligence, Orchestration and Control. An efficient implementation of such control loop allows the network to timely adapt to changes and maintain the required quality of service.Many solutions for collecting network parameters (i.e., implementing ZSM data collection) are proposed that fall under the broad umbrella of network telemetry. An example is the Google gRPC, that represented one of the first solutions to provide a framework for data collection. Since then, the number of available frameworks is proliferating. In this paper we propose the utilisation of Apache Kafka as a framework for collecting optical network parameters. Then, the paper goes beyond that by proposing and showing how Apache Kafka can be effective for supporting data exchange and management of whole ZSM closed-loop.Experimental evaluation results show that, even when a large number of data are collected, the solution is scalable and the time to disseminate the parameter values is short. Indeed, the difference between the reception time and the generation time of data is, on average, 40-50ms when about four thousand messages are generated

Next generation control of transport networks

It is widely understood by telecom operators and industry analysts that bandwidth demand is increasing dramatically, year on year, with typical growth figures of 50% for Internet-based traffic [5]. This trend means that the consumers will have both a wide variety of devices attaching to their networks and a range of high bandwidth service requirements. The corresponding impact is the effect on the traffic engineered network (often referred to as the “transport network”) to ensure that the current rate of growth of network traffic is supported and meets predicted future demands. As traffic demands increase and newer services continuously arise, novel network elements are needed to provide more flexibility, scalability, resilience, and adaptability to today’s transport network. The transport network provides transparent traffic engineered communication of user, application, and device traffic between attached clients (software and hardware) and establishing and maintaining point-to-point or point-to-multipoint connections. The research documented in this thesis was based on three initial research questions posed while performing research at British Telecom research labs and investigating control of transport networks of future transport networks: 1. How can we meet Internet bandwidth growth yet minimise network costs? 2. Which enabling network technologies might be leveraged to control network layers and functions cooperatively, instead of separated network layer and technology control? 3. Is it possible to utilise both centralised and distributed control mechanisms for automation and traffic optimisation? This thesis aims to provide the classification, motivation, invention, and evolution of a next generation control framework for transport networks, and special consideration of delivering broadcast video traffic to UK subscribers. The document outlines pertinent telecoms technology and current art, how requirements I gathered, and research I conducted, and by which the transport control framework functional components are identified and selected, and by which method the architecture was implemented and applied to key research projects requiring next generation control capabilities, both at British Telecom and the wider research community. Finally, in the closing chapters, the thesis outlines the next steps for ongoing research and development of the transport network framework and key areas for further study

A situational awareness model for data analysis on 5G mobile networks : the SELFNET analyzer framework

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Informática, Departamento de Ingeniería del Software e Inteligencia Artificial, leída el 14-07-2017Se espera que las redes 5G provean un entorno seguro, con able y de alto rendimiento con interrupciones m nimas en la provisi on de servicios avanzados de red, sin importar la localizaci on del dispositivo o cuando el servicio es requerido. Esta nueva generaci on de red ser a capaz de proporcionar altas velocidades, baja latencia y mejor Calidad de Servicio (QoS) comparado con las redes actuales Long Term Evolution (LTE). Para proveer estas capacidades, 5G propone la combinaci on de tecnolog as avanzadas tales como Redes De nidas por Software (SDN), Virtualizaci on de las Funciones de Red (NFV), Redes auto-organizadas (SON) e Inteligencia Arti cial. De manera especial, 5G ser a capaz de solucionar o mitigar cambios inesperados o problemas t picos de red a trav es de la identi caci on de situaciones espec cas, tomando en cuenta las necesidades del usuario y los Acuerdos de Nivel de Servicio (SLAs). Actualmente, los principales operadores de red y la comunidad cient ca se encuentran trabajando en estrategias para facilitar el an alisis de datos y el proceso de toma de decisiones cuando eventos espec cos comprometen la salud de las redes 5G. Al mismo tiempo, el concepto de Conciencia Situacional (SA) y los modelos de gesti on de incidencias aplicados a redes 5G est an en etapa temprana de desarrollo. La idea principal detr as de estos conceptos es prevenir o mitigar situaciones nocivas de manera reactiva y proactiva. En este contexto, el proyecto Self-Organized Network Management in Virtualized and Software De ned Networks (SELFNET) combina los conceptos de SDN, NFV and SON para proveer un marco de gesti on aut onomo e inteligente para redes 5G. SELFNET resuelve problemas comunes de red, mientras mejora la calidad de servicio (QoS) y la Calidad de Experiencia (QoE) de los usuarios nales...5G networks hope to provide a secure, reliable and high-performance environment with minimal disruptions in the provisioning of advanced network services, regardless the device location or when the service is required. This new network generation will be able to deliver ultra-high capacity, low latency and better Quality of Service (QoS) compared with current Long Term Evolution (LTE) networks. In order to provide these capabilities, 5G proposes the combination of advanced technologies such as Software De ned Networking (SDN), Network Function Virtualization (NFV), Self-organized Networks (SON) or Arti cial Intelligence. In particular, 5G will be able to face unexpected changes or network problems through the identi cation of speci c situations, taking into account the user needs and the Service Level Agreements (SLAs). Nowadays, the main telecommunication operators and community research are working in strategies to facilitate the data analysis and decision-making process when unexpected events compromise the health in 5G Networks. Meanwhile, the concept of Situational Awareness (SA) and incident management models applied to 5G Networks are also in an early stage. The key idea behind these concepts is to mitigate or prevent harmful situations in a reactive and proactive way. In this context, Self-Organized Network Management in Virtualized and Software De ned Networks Project (SELFNET) combines SDN, NFV and SON concepts to provide a smart autonomic management framework for 5G networks. SELFNET resolves common network problems, while improving the QoS and Quality of Experience (QoE) of end users...Depto. de Ingeniería de Software e Inteligencia Artificial (ISIA)Fac. de InformáticaTRUEunpu

Building the Infrastructure for Cloud Security

Computer scienc