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Description:
Future visions of the Internet of Things and Industry 4.0 demand for large scale deployments of mobile devices while removing the numerous disadvantages of using batteries: degradation, scale, weight, pollution, and costs. However, this requires computing platforms with extremely low energy consumptions, and thus employ ultra-low-power hardware, energy harvesting solutions, and highly efficient power-management hardware and software. The goal of these power management solutions is to either achieve power neutrality, a condition where energy harvest and energy consumption equalize while maximizing the service quality, or to enhance power efficiency for conserving energy reserves. To reach these goals, intelligent power-management decisions are needed that utilize precise energy data. This thesis discusses the measurement of energy in embedded systems, both online and by external equipment, and the utilization of the acquired data for modeling the power consumption states of each involved hardware component. Furthermore, a method is shown to use the resulting models by instrumenting preexisting device drivers. These drivers enable new functionalities, such as online energy accounting and energy application interfaces, and facilitate intelligent power management decisions. In order to reduce additional efforts for device driver reimplementation and the violation of the separation of concerns paradigm, the approach shown in this thesis synthesizes instrumentation aspects for an aspect oriented programming language, so that the original device-driver source code remains unaffected. Eventually, an automated process of energy measurement and data analysis is presented. This process is able to yield precise energy models with low manual effort. In combination with the instrumentation synthesis of aspect code, this method enables an accelerated creation process for energy models of ultra-low-power systems. For all proposed methods, empirical accuracy and overhead measurements are presented. To support the claims of the ...