Abstract-The fact that energy is a scarce resource in many embedded real-time systems creates the need for energy-aware task schedulers, which not only guarantee timing constraints but also consider energy consumption. Unfortunately, existing approaches to analyze the worst-case execution time (WCET) of a task usually cannot be directly applied to determine its worstcase energy consumption (WCEC) due to execution time and energy consumption not being closely correlated on many stateof-the-art processors. Instead, a WCEC analyzer must take into account the particular energy characteristics of a target platform.In this paper, we present 0g, a comprehensive approach to WCEC analysis that combines different techniques to speed up the analysis and to improve results. If detailed knowledge about the energy costs of instructions on the target platform is available, our tool is able to compute upper bounds for the WCEC by statically analyzing the program code. Otherwise, a novel approach allows 0g to determine the WCEC by measurement after having identified a set of suitable program inputs based on an auxiliary energy model, which specifies the energy consumption of instructions in relation to each other. Our experiments for three target platforms show that 0g provides precise WCEC estimates.
Energy-neutral real-time systems harvest the entire energy they use from their environment. In such systems, energy must be treated as an equally important resource as time, which creates the need to solve a number of problems that so far have not been addressed by traditional real-time systems. In particular, this includes the scheduling of tasks with both time and energy constraints, the monitoring of energy budgets, as well as the survival of blackout periods during which not enough energy is available to keep the system fully operational.
In this article, we address these issues presenting E
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OS, an operating-system kernel for energy-neutral real-time systems. E
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OS considers mixed time criticality levels for different energy criticality modes, which enables a decoupling of time and energy constraints when one is considered less critical than the other. When switching the energy criticality mode, the system also changes the set of executed tasks and is therefore able to dynamically adapt its energy consumption depending on external conditions. By keeping track of the energy budget available, E
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OS ensures that in case of a blackout the system state is safely stored to persistent memory, allowing operations to resume at a later point when enough energy is harvested again.
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