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Over the last years, the issue of reducing energy consumption in embedded system applications has received considerable attention from the scientific community, since responsiveness and low energy consumption are often conflicting requirements. In this context, this dissertation proposes a methodology applied in early design phases for supporting design decisions on energy consumption and performance of embedded applications. In addition, this work proposes temporized discrete event models that have been evaluated through a stochastic simulation approach to represent different system scenarios in an easier way. For each scenario, it is important to decide the maximum number of simulations and the duration of each simulation, where both may impact the performance estimates. Such approach also considers an intermediate model which represents the system behavioral description and, through these models, the scenarios are analyzed. The intermediate model is based on timed Colored Petri Net, a formal behavioral model that not only allows the software execution analysis, but it is also supported by a set of well established methods for property verifications. In this context, a software, named ALUPAS, for estimating energy consumption and execution time of embedded systems is presented. Lastly, a real-world case study as well as customized examples are presented, showing the applicability of this work in which non-specialized users do not need to interact directly with the Petri net formalism.

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Abstract-Hundreds of natural disasters occur in many parts of the world every year, causing billions of dollars in damages. This fact contrasts with the high availability requirement of cloud computing systems, and, to protect such systems from unforeseen catastrophe, a recovery plan requires the utilization of different data centers located far enough apart. However, the time to migrate a VM from a data center to another increases due to distance. This work presents dependability models for evaluating distributed cloud computing systems deployed into multiple data centers considering disaster occurrence. Additionally, we present a case study which evaluates several scenarios with different VM migration times and distances between data centers.

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