In this paper we propose a novel scheduling framework for a dynamic real-time environment that experiences power consumption
constraints. This framework is capable of dynamically adjusting the voltage/speed of the system, such that no task in the
system misses its deadline and the total energy savings of the system are maximized.
Each task in the system consumes a certain amount of energy, which depends on a speed chosen for execution. The process of
selecting speeds for execution while maximizing the energy savings of the system requires the exploration of a large number
of combinations, which is too time consuming to be computed on-line. Thus, we propose an integrated heuristic methodology
which executes an optimization procedure and an approximate greedy algorithm in a low computation time. This scheme allows
the scheduler to handle power-aware real-time tasks with low cost while maximizing the use of the available resources and
without jeopardizing the temporal constraints of the system. Simulation results show that our heuristic methodology achieves
a performance with near-optimal results.