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In this paper, we address power-aware scheduling of periodic tasks to reduce CPU energy consumption in hard real-time systems through dynamic voltage scaling. Our intertask voltage scheduling solution includes three components: (a) a static (off-line) solution to compute the optimal speed, assuming worst-case workload for each arrival, (b) an online speed reduction mechanism to reclaim energy by adapting to the actual workload, and (c) an on-line, adaptive and speculative speed adjustment mechanism to anticipate early completions of future executions by using the average-case workload information. All these solutions still guarantee that all deadlines are met. Our simulation results show that our reclaiming algorithm alone outperforms other recently proposed inter-task voltage scheduling schemes. Our speculative techniques are shown to provide additional gains, approaching the theoretical lower-bound by a margin of 10%.

In this paper, we provide a n efficient solution for periodic real-time tasks with (potentially) different power consumption characteristics. We show that, a task T, can run a t a constant speed 5';. at every instance without hurting optimality. We sketch an O(n2 log n ) algorithm to compute the optimal S;. values. We also prove that the EDF (Earliest Deadline First) scheduling policy can be used to obtain a feasible schedule with these optimal speed values.

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.

Abstract