Energy-aware task allocation for rate monotonic scheduling

We consider the problem of energy minimization for periodic preemptive hard real-time tasks that are scheduled on an identical multiprocessor platform with dynamic voltage scaling capability. We adopt partitioned scheduling and assume that the tasks are assigned rate-monotonic priorities. We show that the problem is NP-hard in the strong sense on m ≥ 2 processors even when the feasibility is guaranteed a priori. Because of the intractability of the problem, we propose an integrated approach that consists of three different components: RMS admission control test, the partitioning heuristic and the speed assignment algorithm. We discuss possible options for each component by considering state-of-the-art solutions. Then, we experimentally investigate the impact of heuristics on feasibility, energy and feasibility/energy performance dimensions. In offline settings where tasks can be ordered according to the utilization values, we show that worst-fit dominates other well-known heuristics. For online settings, we propose an algorithm that is based on reserving a subset of processors for light tasks to guarantee a consistent performance.