Arquiteturas de referência referem-se a um tipo especial de arquitetura de software que captura a essência das arquiteturas de uma coleção de sistemas de um dado domínio. O propósito de uma arquitetura de referência é principalmente prover suporte para o desenvolvimento, padronização e evolução das arquiteturas de sistemas de software. Projetadas para vários domínios e propósitos, essas arquiteturas têm impactado importantes aspectos do desenvolvimento de sistemas de software, tais como a produtividade e a qualidade desses sistemas.
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The memory system is a fundamental performance and energy bottleneck in almost all computing systems. Recent system design, application, and technology trends that require more capacity, bandwidth, efficiency, and predictability out of the memory system make it an even more important system bottleneck. At the same time, DRAM and flash technologies are experiencing difficult technology scaling challenges that make the maintenance and enhancement of their capacity, energy-efficiency, and reliability significantly more costly with conventional techniques.
Success on analysis and construction of any system depends on a good model which allows to represent components, properties, relationships and consequently features and behaviors. As ontologies are the way to represent data with their associated meaning (semantics) and inference logic, it is a good foundation for creating system models which are computable. They can be stored and processed using ontology technologies as defined on Web Semantic arena.
Many systems today depend on a kind of software to operate and the quality of the overall system is related to the quality of software delivered with it. Testing and formal verification are the most used techniques to certify the quality of software today. However, how to decide which technique should be used to guarantee software quality? Can we use both techniques in a single project? which one is better? cheaper? scalable? which level, design or code?
The potential of evolutionary algorithms in multiobjective optimization was identified early in their history. That potential has been realized over the years with the development of increasingly elaborate Evolutionary Multiobjective Optimization (EMO) algorithms that have found many important applications in the real world, and have contributed significantly to the growth in popularity of multiobjective optimization in general.