Adaptive Scheduling Server for Power-Aware Real-Time Tasks

In this paper, we propose a novel scheduling framework for a dynamic real-time environment with energy constraints. This framework dynamically adjusts the CPU voltage/frequency so that no task in the system misses its deadline and the total energy savings of the system are maximized. In this paper, we consider only realistic, discrete-level speeds. 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 online. Thus, we propose an integrated heuristic methodology, which executes an optimization procedure 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 is able to generate power-aware scheduling solutions with near-optimal performance.