A Probabilistic and energy-efficient scheduling approach for online application in real-time systems

This work considers the problem of minimizing the power consumption for real-time scheduling on processors with discrete operating modes. We provide a model for determining the expected energy demand based on statistical execution profiles which considers both the current and subsequent tasks. If the load after the execution of the current task is expected to be high and slack time is conserved for subsequent tasks, we are able to derive an optimal solution to the energy minimization problem. For the remaining cases we propose a heuristic approach that also achieves a low run time overhead. In contrast to previous work, our scheduling approach is not restricted to single task scenarios, frame-based real-time systems, or pre-computed schedules. Simulations and comparisons with energy-efficient schedulers from literature demonstrate the efficiency of our approach.