Modelagem de preços de provedores de IaaS utilizando regressão múltipla

Uma alternativa para usuários reduzirem custos de aquisição e manutenção de infraestrutura computacional para desenvolver, implementar e executar suas aplicações é a computação em nuvem. Os serviços de computação em nuvem são oferecidos por provedores e podem ser classificados em três modalidades: Platform-as-a-Service (PaaS), Software-as-a-Service (SaaS) e Infrastructure-as-a-Service (IaaS). Em IaaS, os provedores oferecem os serviços divididos em instâncias e o usuário tem à disposição uma máquina virtual com os recursos computacionais que desejar a um determinado valor. O principal desafio enfrentado pelas empresas é escolher, além do provedor, a instância que melhor se adapta as suas necessidades. Frequentemente, estas empresas precisam de uma grande infraestrutura computacional para gerir e aperfeiçoar seus processos de negócio e, diante do alto custo para manter uma infraestrutura local, têm migrado suas aplicações para a nuvem. Este trabalho busca fornecer subsídios capazes de auxiliar as empresas no processo de seleção do melhor provedor/instância para implantar e executar suas soluções de integração na nuvem. Para isso, um estudo preliminar para a elaboração de uma nova proposta de modelagem dos preços das instâncias de máquinas virtuais usando regressão linear é apresentado. Nesta abordagem são considerados os provedores Amazon EC2, Google Compute Engine e Microsoft Windows Azure.info:eu-repo/semantics/acceptedVersio

Description and discrimination of freshness and biometric qualities of three different fishes: Grass carp, pacu, and catfish

The goal of this work was to wade into the freshness quality and biometric evaluation, by means of distinct statistical descriptive methods, on three fresh catch species of fish, as well as to evaluate the discriminant potential of the variables targeted in the study. The species grass carp (Ctenopharyngodon idella), pacu (Piaractus mesopotamicus), and catfish (Ictalurus punctatus) were caught at a rural property located in the city of Pato Branco, PR, Brazil. These fresh catch were weighed, measured, eviscerated, and cut into fillets for acquisition of biometric parameters. Freshness was judged by the analysis of the total volatile basic nitrogen (TVB-N) value and pH. The comparison between means and medians showed symmetries for biometric measures. Correlations between body measures and fillet yield showed a weak relation regardless of the species analysed, wherein the best equation for predictions was obtained by relating total weight to the fillet's weight. The biometric variables were the best discriminants for the species

Selecting services in the cloud: a decision support methodology focused on infrastructure-as-a-service context

Growing demand for reduced local hardware infrastructure is driving the adoption of Cloud Computing. In the Infrastructure-as-a-Service model, service providers offer virtualized computational resources in the form of virtual machine instances. The existence of a large variety of providers and instances makes the decision-making process a difficult task for users, especially as factors such as the datacenter location - where the virtual machine is hosted - have a direct influence on the price of instances. The same instance may present price differences when hosted in different geographically distributed datacenters and, because of that, the datacenter location needs to be taken into account through the decision-making process. Given this problem, we propose the D-AHP, a methodology to aid decision-making based on Pareto Dominance and Analytic Hierarchy Process (AHP). In the D-AHP, the dominance concept is applied to reduce the number of instances to be compared; the instances selection is based on a set of objectives, while AHP ranks the selected ones from a set of criteria and sub-criteria, among them the datacenter location. The results from case studies show that differences may arise in the results, regarding which instance is more suitable for the user, when considering the datacenter location as a criterion to choose an instance. This fact highlights the need to consider this factor during the process of migrating applications to the Cloud. In addition, Pareto Dominance applied early over the set of total instances has proved to be efficient, once it significantly reduces the number of instances to be compared and ordered by the AHP by excluding instances with less computational resources and higher cost in the decision-making process, mainly for larger application workloads.info:eu-repo/semantics/acceptedVersio