Performance evaluation of auto scaling mechanisms in private clouds for supporting a web service application

• Ano de defesa: 2015
• Autor(a) principal: CAMPOS, Eliomar Gomes
• Orientador(a): MACIEL, Paulo Romero Martins
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Universidade Federal de Pernambuco
• Programa de Pós-Graduação: Programa de Pos Graduacao em Ciencia da Computacao
• País: Brasil
• Palavras-chave em Português: Computação em Nuvem; Nuvem Privada; Escala Automática; Avaliação de Desempenho; Modelagem Analítica; Cloud Computing; Private Cloud; Auto Scaling; Performance Evaluation; Analytical Modeling
• Link de acesso: https://repositorio.ufpe.br/handle/123456789/15883

Composite web services, also known as mashups, are useful to build added-value products in the web. Cloud computing environments have been widely used for hosting web services due to the possibility of increasing or decreasing available resources through automatic mechanisms (i.e.: auto scaling). Such elastic behavior ease the task of reaching satisfactory performance on peaks of demand without wasting resources. It is hard to determine the right components to tune such systems performance when eventually needed. This study evaluates the performance of auto scaling mechanisms for private clouds hosting an event recommendation web service. A hierarchical modeling approach is used to cope with the complexity of such a system, and represent specific details of these mechanisms. Our study applies parametric sensitivity analysis from several performance metrics of the models, such as mean execution time of the auto scaling monitoring, mean time of VMs instantiation, and the mean response time perceived by the web service user. We also have carried a General Full Factorial Experiment, in order to calculate the relevance and effects of each factor involved in the processes of auto scaling and virtual machines (VMs) instantiation. For the auto scaling monitoring, we analyze the factors: collection period of a metric, number of monitored virtual machines, and the time of monitoring of a metric. Regarding the instantiation process, the following factors have been chosen: VM type, VM image size, and VM caching. This analysis allows checking the impact of parameters on the system response time and pointing out effective ways for improvement of performance.