Rede de acesso virtualizada: alocação e posicionamento de recursos

• Ano de defesa: 2018
• Autor(a) principal: Souza, Phelipe Alves de
• Orientador(a): Cardoso, Kleber Vieira
• Banca de defesa: Cardoso, Kleber Vieira, Bueno, Elivelton Ferreira, Abousheaisha, Abdallah S. Abdallah, Pinto, Leizer de Lima, Klautau Júnior, Aldebaro Barreto da Rocha
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Goiás
• Programa de Pós-Graduação: Programa de Pós-graduação em Ciência da Computação (INF)
• Departamento: Instituto de Informática - INF (RG)
• País: Brasil
• Palavras-chave em Português: CRAN; NFV; RAN; LTE; SDN; Virtualização; Alocação de recurso
• Palavras-chave em Inglês: CRAN; NFV; RAN; LTE; SDN; Virtualization; Resource allocation
• Área do conhecimento CNPq: CIENCIA DA COMPUTACAO::SISTEMAS DE COMPUTACAO
• Link de acesso: http://repositorio.bc.ufg.br/tede/handle/tede/9030

There are great expectations in CRAN and network virtualization (NFV) technologies, and especially in view of the potential they have to accelerate the deployment of new services while lowering the costs of network operators. Several papers discussed the benefits of deploying a new network infrastructure with such technologies, but only a few investigated how the transition from a legacy network could be. In this context, there is a relevant problem that involves three main issues: 1) which network locations should be updated; 2) how to update the selected location, \ie, to fully virtualized or not; and 3) who should attend virtualized sites. These issues are influenced by the level of centralization employed in a given access network (RAN). Here we propose two optimization models and two heuristics that allow the decision maker to define the desired level of centralization and to evaluate its impact on some metrics such as the investment needed and the level of centralization actually achieved. The models show how the investment should be applied according to the level of centralization and the relative cost between the different resources. Our heuristics present similar performance to the exact approach for relatively small scenarios of the problem, but are able to solve topologies of networks with large number of vertices and maintain a satisfactory solution close to the ideal.