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Dynamic voltage scaling (DVS) for real-time systems has been extensively studied to save energy. Previous studies consider the probabilistic distributions of tasks' execution time to assist DVS in task scheduling. These studies use probability information for intra-task frequency scheduling but do not sufficiently explore the opportunities for intertask scheduling to save more energy. This paper presents a new approach to integrate intra-task and inter-task frequency scheduling for better energy savings in hard realtime systems with uncertain task execution time. Our approach has two steps: (a) We calculate statistically optimal frequency schedules for multiple periodic tasks using earliest deadline first (EDF) scheduling for processors that can change frequencies continuously. (b) For processors with a limited range of discrete frequencies, we further present a heuristic algorithm to construct frequency schedules. Our evaluation shows that our approach saves up to 23% more energy than existing solutions.

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Power management saves power by shutting down idle devices. These devices often serve requests from concurrently running tasks. Ordering task execution can adjust the lengths of idle periods and exploit better opportunities for power management. This paper presents an on-line low-power scheduling algorithm for multiple devices. Simulations show that it can save up to 33% power and reduce 40% state-transition delays. This algorithm is robust under imperfect knowledge of future requests and timing constraints; therefore, it is applicable to interactive systems.

No abstract