No abstract

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%.

Abstract

Abstract

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.