The advent of services such as cloud computing, social networks and e-commerce has led to an increased demand for computer resources from data centers. Prominent issues for data center designers are sustainability, cost, and dependability, which are significantly affected by the redundant architectures required to support these services. Within this context, models are important tools for designers when attempting to quantify these issues before implementing the final architecture. This thesis proposes a set of models for the integrated quantification of the sustainability impact, cost, and dependability of data center power and cooling infrastructures. This is achieved with the support of an evaluation environment which is composed of ASTRO, Mercury and Optimization tools. The approach taken to perform the system dependability evaluation employs a hybrid modeling strategy which recognizes the advantages of both stochastic Petri nets and reliability block diagrams. Besides that, a model is proposed to verify that the energy flow does not exceed the maximum power capacity that each component can provide (considering electrical devices) or extract (assuming cooling equipment). Additionally, an optimization method is proposed for improving the results obtained by Reliability Block Diagrams, Stochastic Petri nets and Energy Flow models through the automatic selection of the appropriate devices from a list of candidate components. This list corresponds to a set of alternative components that may compose the data center architecture. Several case studies are presented that analyze the environmental impact and dependability metrics as well as the operational energy cost of real-world data center power and cooling architectures. / Submitted by João Arthur Martins (joao.arthur@ufpe.br) on 2015-03-12T18:54:10Z No. of bitstreams: 2 Tese Gustavo Callou.pdf: 4626749 bytes, checksum: 336a34ffc39f6ac623fa1144de2a66bf (MD5) license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) / Made available in DSpace on 2015-03-12T18:54:10Z (GMT). No. of bitstreams: 2 Tese Gustavo Callou.pdf: 4626749 bytes, checksum: 336a34ffc39f6ac623fa1144de2a66bf (MD5) license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Previous issue date: 2013-11-12 / O surgimento de servi¸cos como computa¸c˜ao nas nuvens, redes sociais e com´ercio eletrˆonico tem aumentado a demanda por recursos computacionais dos data centers. Preocupa¸c˜oes decorrentes para os projetistas de data center s˜ao sustentabilidade, custo, e dependabilidade, os quais s˜ao significativamente afetados pelas arquiteturas redundantes requeridas para suportar tais servi¸cos. Nesse contexto, modelos s˜ao ferramentas importantes para projetistas quanto a tentativa de quantificar esses problemas antes mesmo de implementar a arquitetura final. Nessa tese, um conjunto de modelos ´e proposto para a quantifica¸c˜ao integrada do impacto na sustentabilidade, custo e dependabilidade das infraestruturas de refrigeramento e potˆencia de data centers. Isso ´e obtido com o suporte do ambiente de avalia¸c˜ao que ´e composto pelas ferramentas ASTRO, Mercury e o m´odulo de otimiza¸c˜ao. A avalia¸c˜ao de dependabilidade faz uso de uma estrat´egia de modelagem h´ıbrida que usa as vantagens tanto das redes de Petri estoc´asticas como dos diagramas de blocos de confiabilidade. Al´em disso, um modelo ´e proposto para realizar a verifica¸c˜ao se fluxo de energia n˜ao excede a capacidade m´axima de potˆencia que cada equipamento pode prover (considerando dispositivos el´etricos) ou extrair (assumindo equipamentos de refrigera¸c˜ao). Adicionalmente, um m´etodo de otimiza¸c˜ao ´e proposto para melhorar os resultados obtidos atrav´es dos diagramas de blocos de confiabilidade, das redes de Petri estoc´asticas e do modelo de fluxo de energia pela sele¸c˜ao autom´atica dos dispositivos apropriados a partir da lista de componentes candidatos. Essa lista corrresponde a um conjunto de componentes que podem ser utilizados para compor a arquitetura de data center. V´arios estudos de casos s˜ao apresentados para analisar o impacto ambiental, a dependabilidade e o custo operacional de energia el´etrica de arquiteturas reais de potˆencia e refrigera¸c˜ao de data centers.