This paper presents a cloud approach for low cost capacity planning evaluations. To perform these evaluations we have to specify and measure the workload on the target system to discover issues and make the necessary adjustments. However, due to high costs, these evaluations are usually done using simulations, which does not consider stochastic effects. We propose to use a tool named PEESOS, a generic and flexible approach to apply real workloads and measure used resources on these real systems. As a proof of concept, our case study use a real ticket sales service to evaluate the influence of scalability in the resource provisioning to show how PEESOS can lower the cost of such real evaluations. The results show the efficiency and savings that we can obtain using PEESOS for large-scale capacity planning evaluations before the real services are deployed. This approach can avoid several problems that real services faces when they launch.
As ferramentas de virtualização têm um papel fundamental no crescimento da utilização da Computação em Nuvem. Por meio da virtualização, é possível realizar a migração de máquinas virtuais dentro de um provedor de serviços de nuvem, proporcionando a utilização eficiente dos recursos. Entretanto, não oram evidenciadas quais técnicas são mais indicadas de acordo com os cenários distintos de carga no qual o sistema está operando. Diferentemente das avaliações de desempenhos simplesmente comparativas encontradas na literatura, este trabalho propõe a utilização de um modelo estatístico consistente para avaliação de desempenho das técnicas de migração: (i) live migration e (ii) non-live migration. O objetivo do modelo estatístico é identificar o comportamento das técnicas de migração de máquinas virtuais sob diferentes situações de cargas de trabalho. O modelo estatístico proposto para utilização é composto pela abordagem de avaliação de desempenho das técnicas de migração de máquina virtual combinado com o planejamento de experimentos de projeto fatorial 2k.Palavras-chave: Migração. Máquinas Virtuais. Avaliação de Desempenho. AbstractPerformance evaluation of virtual machine migration approaches on cloud computign environment Virtualization tools play a key role in increasing the use of Cloud Computing. Through virtualization, it is possible to migrate virtual machines within a cloud service provider, providing efficient resource utilization. However, there are not evident which techniques work better in according to the different load scenarios in which the system is operating. This work proposes the use of a consistent statistical model to evaluate the performance of migration techniques:(i) live migration and (ii) non-live migration. The objective of the statistical model is to identify the behavior of virtual machine migration techniques under different workload situations. The proposed statistical model for use is based on the performance evaluation approach of the virtual machine migration techniques combined with the 2k factorial design.Keywords: Migration. Performance Evaluation. Virtual Machine.
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