Energy-efficient dynamic task scheduling algorithms for DVS systems

Abstract

Dynamic voltage scaling (DVS) is a well-known low-power design technique that reduces the processor energy by slowing down the DVS processor and stretching the task execution time. However, in a DVS system consisting of a DVS processor and multiple devices, slowing down the processor increases the device energy consumption and thereby the system-level energy consumption. In this paper, we first use system-level energy consideration to derive the “optimal ” scaling factor by which a task should be scaled if there are no deadline constraints. Next, we develop dynamic task-scheduling algorithms that make use of dynamic processor utilization and optimal scaling factor to determine the speed setting of a task. We present algorithm duEDF , which reduces the CPU energy consumption and algorithm duSYS and its reduced preemption version, duSYS_PC , which reduce the system-level energy. Experimental results on the video-phone task set show that when the CPU power is dominant, algorithm duEDF results in up to 45% energy savings compared to the non-DVS case. When the CPU power and device power are comparable, algorithms duSYS and duSYS_PC achieve up to 25% energy saving compared to CPU energy-efficient algorithm duEDF , and up to 12% energy saving over the non-DVS scheduling algorithm. However, if the device power is large compared to the CPU power, then we show that a DVS scheme does not result in lowest energy. Finally, a comparison of the performance of algorithms duSYS and duSYS_PC show that preemption control has minimal effect on system-level energy reduction.