Grids, multi-clusters, NUMA systems, and ad-hoc collections of distributed computing devices all present diverse environments in which distributed computing applications can be run. Due to the diversity of features provided by these environments a distributed application that is to perform well must be specifically designed and optimised for the environment in which it is deployed. Such optimisations generally affect the application's communication structure, its consistency protocols, and its communication protocols. This thesis explores approaches to improving the ability of distributed applications to share consistent data efficiently and with improved functionality over wide-area and diverse environments. We identify a fundamental separation of concerns for distributed applications. This is used to propose a new model, called the view model, which is a hybrid, cost-conscious approach to remote data sharing. It provides the necessary mechanisms and interconnects to improve the flexibility and functionality of data sharing without defining new programming models or protocols. We employ the view model to adapt distributed applications to their run-time environment without modifying the application or inventing new consistency or communication protocols. We explore the use of view model properties on several programming models and their consistency protocols. In particular, we focus on programming models used in distributed-shared-memory middleware and applications, as these can benefit significantly from the properties of the view model. Our evaluation demonstrates the benefits, side effects and potential short-comings of the view model by comparing our model with traditional models when running distributed applications across several multi-clusters scenarios. In particular, we show that the view model improves the performance of distributed applications while reducing resource usage and communication overheads.

[pt] A implantação de aplicações baseadas em componentes distribuídos é composta por um conjunto de atividades geridas por uma Infraestrutura de Implantação. Aplicações atuais estão se tornando cada vez mais complexas, necessitando de um ambiente alvo dinâmico e multi-plataforma. Assim, a atividade de planejamento de uma implantação é o passo mais crítico, pois define a configuração da infraestrutura de execução de forma a atender os requisitos do ambiente alvo de uma aplicação. Por outro lado, o modelo de serviço na nuvem chamado Infraestrutura como Serviço(IaaS) oferece recursos computacionais sob demanda, com características dinâmicas, escaláveis e elásticas. Nesta dissertação nós estendemos a Infraestrutura de Implantação para componentes SCS de forma a permitir o uso de nuvens privadas ou públicas como o ambiente alvo de uma implantação, através do uso de uma cloud API e políticas flexíveis para especificar um ambiente alvo personalizado. Além disso, hospedamos a infraestrutura de implantação na nuvem. Isto permitiu-nos usar recursos computacionais sob demanda para instanciar os serviços da Infraestrutura de Implantação, produzindo uma Plataforma como Serviço(PaaS) experimental. / [en] Deployment of distributed component-based applications is composed of a set of activities managed by a Deployment Infrastructure. Current applications are becoming increasingly more complex, requiring a multi-platform and a dynamic target environment. Thus, the planning activity is the most critical step because it defines the configuration of the execution infrastructure in order to satisfy the requirements of the application’s target environment. On the other hand, the cloud service model called Infrastructure as a Service (IaaS) offers on-demand computational resources with dynamic, scalable, and elastic features. In this work we have extended the Deployment Infrastructure for SCS componentes to support private or public clouds as its target environment, through the use of a cloud API and flexible policies to specify a customized target environment. Additionally, we host the Deployment Infrastructure on the cloud, which allow us to use on-demand computational resources to instantiate Deployment Infrastructure services, creating an experimental Platform as a Service (PaaS).

The use of distributed systems, involving multiple components, has become a common industry practice. However, modeling the behaviour of such systems is a challenge, especially when the behavior consists of several collaborations of different parties, each involving possibly several starting (input) and ending (output) events of the involved components. Furthermore, the global behavior should be described as a composition of several sub-behaviours, in the following called collaborations, and each collaboration may be further decomposed into several sub-collaborations. We assume that the performance of the elementary sub-collaborations is known, and that the performance of the global behavior should be determined from the performance of the contained elementary collaborations and the form of the composition. A collaboration, in this thesis, is characterized by a partial order of input and output events, and the performance of the collaboration is defined by the minimum delays required for a given output event with respect to an input event. This is a generalization of the semantics of UML Activities, where all input events are assumed to occur at the same time, and all output events occur at the same time. We give a semantic definition of the dynamic behavior of composed collaborations using the composition operators for control flow from UML Activity diagrams, in terms of partial order relationships among the involved input and output events. Based on these semantics, we provide formulas for calculating the performance of composed collaborations in terms of the performance of the sub-collaborations, where each delay is characterized by (a) a fixed value, (b) a range of values, and (c) a distribution (in the case of stochastic behaviours). We also propose approximations for the case of stochastic behavior with Normal distributions, and discuss the expected errors that may be introduced due to ignoring of shared resources or possible dependencies in the case of stochastic behaviours. A tool has been developed for evaluating the performance of complex collaborations, and examples and case studies are discussed to illustrate the applicability of the performance analysis and the visual notation which we introduced for representing the partial-order relationships of the input and output events.

The use of distributed systems, involving multiple components, has become a common industry practice. However, modeling the behaviour of such systems is a challenge, especially when the behavior consists of several collaborations of different parties, each involving possibly several starting (input) and ending (output) events of the involved components. Furthermore, the global behavior should be described as a composition of several sub-behaviours, in the following called collaborations, and each collaboration may be further decomposed into several sub-collaborations. We assume that the performance of the elementary sub-collaborations is known, and that the performance of the global behavior should be determined from the performance of the contained elementary collaborations and the form of the composition. A collaboration, in this thesis, is characterized by a partial order of input and output events, and the performance of the collaboration is defined by the minimum delays required for a given output event with respect to an input event. This is a generalization of the semantics of UML Activities, where all input events are assumed to occur at the same time, and all output events occur at the same time. We give a semantic definition of the dynamic behavior of composed collaborations using the composition operators for control flow from UML Activity diagrams, in terms of partial order relationships among the involved input and output events. Based on these semantics, we provide formulas for calculating the performance of composed collaborations in terms of the performance of the sub-collaborations, where each delay is characterized by (a) a fixed value, (b) a range of values, and (c) a distribution (in the case of stochastic behaviours). We also propose approximations for the case of stochastic behavior with Normal distributions, and discuss the expected errors that may be introduced due to ignoring of shared resources or possible dependencies in the case of stochastic behaviours. A tool has been developed for evaluating the performance of complex collaborations, and examples and case studies are discussed to illustrate the applicability of the performance analysis and the visual notation which we introduced for representing the partial-order relationships of the input and output events.