01.1 - Graduação (Sede)
URI permanente desta comunidadehttps://arandu.ufrpe.br/handle/123456789/2
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Resultados da Pesquisa
Item Analisando o Backup-as-a-Service como uma estratégia de recuperação de desastres(2021-06-02) Queiroz, Ewerton Cleyton Silva de; Andrade, Ermeson Carneiro de; Mendonça Neto, Júlio Rodrigues de; http://lattes.cnpq.br/7849727159222731; http://lattes.cnpq.br/2466077615273972; http://lattes.cnpq.br/1234353605805269In modern environments, failures in information and communication technology (ICT) systems can have several consequences for a business, like data and revenue loss and customers dissatisfaction. Disaster recovery (DR) solutions, as BackupasaService (BaaS), has been adopted by companies as a way to avoid these problems and assure business continuity. Nevertheless, there are plenty of variables to consider during the adoption of a DR solution. Then, in this work, we present an integrated approach using experiments and models to evaluate a BaaS environment designed for DR. In our analysis, we consider relevant DR metrics like availability, downtime, RTO (Recovery Time Objective), and RPO (Recovery Point Objective). The results demonstrate that once BaaS is applied, the environment availability can vary according to the amount of data needed to be backed up or restored. Furthermore, sensitivity analysis indicates that the time needed to recover the data center and the backup interval are the most important parameter values for metrics like RTO and RPO. The proposed approach can help companies or individuals involved in the decisionmaking process for purchasing a DR solution.Item Um ambiente para modelagem integrada de energia, custo e disponibilidade de Data Centers(2021-07-20) Leonardo, Wenderson de Souza; Callou, Gustavo Rau de Almeida; http://lattes.cnpq.br/3146558967986940; http://lattes.cnpq.br/0100793252761254Sustainability has received increasing attention from the scientific community, with a strong focus on reducing energy consumption and maintaining nonrenewable resources for future generations. In parallel, the expansion of paradigms such as cloud computing, social networking, and e commerce has increased the demand for data centers. In this context, tools that support the modeling of data center architectures and compute metrics such as availability, cost, and energy consumption are extremely important. This project proposes developing a tool with a high level vision for modeling data Center architectures to compute energy consumption, availability, and cost. This tool development uses a graph library to represent the architecture’s components, and from which it will be internally converted to scripts compatible with another tool, called Mercury. The proposed tool will convert the model of this high level view, and through the communication with Mercury, the metrics of interest will be computed by formalism such as RBD(Reliability Block Diagram), EFM (Energy Flow Model) e SPN (Stochastic Petri Nets). Furthermore, optimization algorithms were integrated into the proposed tool. In order to find a combination of components for a given data center architecture in a reduced fraction of time, compared to the brute force algorithm (algorithm that tests all cases), from a preestablished list of components.