Aplicação de séries temporais e redes neurais em um ambiente de computação em nuvem

• Ano de defesa: 2014
• Autor(a) principal: Santos, Tatiana Fernanda Mousquer dos
• Orientador(a): Souza, Adriano Mendonça
• Banca de defesa: Zanini, Roselaine Ruviaro , Franzin, Rozelaine de Fatima
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Santa Maria
• Programa de Pós-Graduação: Programa de Pós-Graduação em Engenharia de Produção
• Departamento: Engenharia de Produção
• País: BR
• Palavras-chave em Português: Séries temporais; Redes neurais; ARIMA; ARMAX; MLP; NARX computação em nuvem
• Palavras-chave em Inglês: Time series; Neurais network; ARIMA; ARMAX; MLP; NARX; Cloud computer
• Área do conhecimento CNPq: CNPQ::ENGENHARIAS::ENGENHARIA DE PRODUCAO
• Link de acesso: http://repositorio.ufsm.br/handle/1/8316

Cloud computing has emerged to change the way computing is offered and used. Instead of having all the necessary hardware and software to manipulate and to store their data, users just need a mechanism to access the Internet. So, the efficient provisioning on demand of computational resources is a challenge to comply with the needs of users. Thus, there is a problem related to the lack of an underlying mechanism to assist a cloud management system to maintain acceptable levels of Quality of Service (QoS) pro-actively. In this context, this work makes a comparative analysis of the predictive ability of different statistical models in seeking to define the most suitable for resource provisioning in a cloud environment. In this way, linear time series techniques namely ARIMA and ARMAX and nonlinear ones based on neural networks so-called MLP and NARX were applied on a dataset of a cluster from Google. The prediction results of usage of cpu, disk and memory shown that the NARX neural network can predict with low error the expected values, being feasible for application in a provisioning mechanism of servers in cloud computing environments.