Um modelo analítico para estimar o consumo de energia de sistemas multi-camadas no nível de transação

• Ano de defesa: 2017
• Autor(a) principal: Ferreira, Alex Rabelo
• Orientador(a): Corrêa, Sand Luz
• Banca de defesa: Corrêa, Sand Luz , Reis, Valéria Quadros dos , Martins, Wellington Santos, Petrucci, Vinicius Tavares
• 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: Modelo analítico; Energia; TPC-W; Sistemas web
• Palavras-chave em Inglês: Analytic model; Energy; TPC-W; Enterprise applications
• Área do conhecimento CNPq: CIENCIA DA COMPUTACAO::SISTEMAS DE COMPUTACAO
• Link de acesso: http://repositorio.bc.ufg.br/tede/handle/tede/7352

In large-scale data centers, power management techniques such as Dynamic Voltage/Frequency Scaling (DVFS), virtual machine consolidation, and power-capping mechanisms promise impressive energy savings compared to traditional resource management strategies. Most of these techniques rely on coarse-grained monitoring of the workload behavior to apply their optimizations. However, coarse-grained monitoring and black box observations are not satisfactory for predicting the behavior of bursty workloads such as those observed in enterprise, Web servers. In this work, we propose an analytical model to estimate the energy consumption of multi-tier Web Systems. Differently from previous works, our model captures the consumption pattern at the level of fine-grained transactions and for each tier of the system. In addition, our model is based only on CPU utilization and server architectural parameters, which can be easily obtained in today’s production environments. We demonstrate the effectiveness of our model in a real-world experimentation environment, based on the TPC-W benchmark. Results show that our model is able to estimate the energy consumption for a Web system with an average relative error of 6.5% in the worst-case scenario, whereas more complex models of the literature present errors within the same order of magnitude.