Adaptive scheduling of real-time systems cosupplied by renewable and nonrenewable energy sources

Energy management is an important issue in today's real-time systems due to the high costs of energy supplying. Using renewable, like wave, wind, and solar energy sources seem promising methods to address this issue. However, because of the existing contrast between the critical nature of hard real-time systems and the unpredictable nature of renewable energies, some supplementary energy source like electricity grid or battery is needed. In this paper, we consider hard real-time systems with two renewable and nonrenewable energy sources. In order to reduce the costs, we present two dynamic voltage scaling controllers to minimize the energy attained from the latter source. In order to handle variations of the environmental energy and workload, the model predictive control approach is employed. One nonlinear approach beside one fast linear piecewise affine explicit controller are proposed. The efficacies of the proposed approaches have been investigated through extensive simulations. Comparisons to an ideal clairvoyant controller as a baseline show that, in the studied scenarios, the proposed controllers guarantee at least 78% of the baseline performance.