Monitorización y descubrimiento para la gestión de redes auto-organizativas en redes virtualizadas y definidas por software
Currently, the growing cost of operational expenditure (OPEX) and capital expenditure (CAPEX) of mobile technologies is constraining the development of new services and applications. The high complexity of the systems requires continuous upgrading and manual re-con guration of the equipments. Operat...
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| Formato: | tesis doctoral |
| Fecha de publicación: | 2018 |
| País: | España |
| Recursos: | Universidad Complutense de Madrid (UCM) |
| Repositorio: | Docta Complutense |
| Idioma: | español |
| OAI Identifier: | oai:docta.ucm.es:20.500.14352/15461 |
| Acesso em linha: | https://hdl.handle.net/20.500.14352/15461 |
| Access Level: | acceso abierto |
| Palavra-chave: | 004.4(043.3) Redes |
| Resumo: | Currently, the growing cost of operational expenditure (OPEX) and capital expenditure (CAPEX) of mobile technologies is constraining the development of new services and applications. The high complexity of the systems requires continuous upgrading and manual re-con guration of the equipments. Operators have to do their best to nd and mitigate a big range of problems, such as link failures, security attacks, Quality of Service degradation, software bugs, among others. In some cases, the only solution is the installation of new hardware equipment and the corresponding partial interruption of service and violations in Service Level Agreements (SLA). In this context, the 5G mobile network architecture is expected to support a big range of advanced requirements, in terms of latency, coverage, bandwidth, security and others. The vision of 5G also includes an integrated self-organized ecosystem of networking, computing and storage resources capable of executing proactive and reactive management tasks. To ful ll this vision, the Self-Organized Network Management in Virtualized and Software De ned Networks SELFNET H2020 project aims to design and implement an autonomic network management framework. The framework will provide self-organizing management capabilities by automatically detect and mitigate network problems that are currently manually solved by network operators. Similarly, SELFNET integrates the self-management paradigm with the use of data mining, learning algorithms, pattern recognition to identify the network behaviour and take actions in order to prevent and minimize several network problems... |
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