Otimização de rotas em redes definidas por software utilizando algoritmos evolucionários

Detalhes bibliográficos
Ano de defesa: 2021
Autor(a) principal: Servílio Souza de Assis
Orientador(a): Não Informado pela instituição
Banca de defesa: Não Informado pela instituição
Tipo de documento: Dissertação
Tipo de acesso: Acesso aberto
Idioma: por
Instituição de defesa: Universidade Federal de Minas Gerais
Brasil
ENG - DEPARTAMENTO DE ENGENHARIA ELÉTRICA
Programa de Pós-Graduação em Engenharia Elétrica
UFMG
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
Link de acesso: http://hdl.handle.net/1843/37790
Resumo: The popularization of cloud services and applications has drastically changed Internet traffic profiles. Such changes have motivated the evolution of the SDN (Software Defined Networking) paradigm, where the data and control planes are separated from the switching elements of the networks, enabling a centralized management view and a series of new applications. From this perspective, optimization models were developed in this work, based on evolutionary algorithms, for routing in best effort and with SLA (Service Level Agreement) scenarios, meeting load balancing and energy efficiency criteria in the use of resources. Optimization models were developed and evaluated in different contexts, considering the quality of the achieved solutions and the performance of the methods. After several analyses, the NSGA-II (Non-dominated Sorting Genetic Algorithm II) was chosen for use in obtaining sets of optimal solutions. In the case of SLA, the objective aggregation technique with a genetic algorithm was chosen, defining a priori preferences. Experiments performed through emulation and simulation indicated an improvement in the performance of the networks, according to the preferences defined by a decision maker, with the algorithm showing the ability to converge to routes that meet the restrictions of the network’s flow demands, also ensuring a minimum use of resources, aiming at energy efficiency.