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. But 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 present dynamic task scheduling algorithms for periodic tasks that minimize the system-level energy (CPU energy + device standby energy). The algorithms use a combination of (i) optimal speed setting, which is the speed that minimizes the system energy for a specific task, and (ii) limited preemption which reduces the numbers of possible preemptions. For the case when the CPU power and device power are comparable, these algorithms achieve up to 43% energy savings compared to [1], but only up to 12% over the non-DVS scheduling. If the device power is large compared to the CPU power, we show that DVS should not be employed.

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.

Battery lifetime enhancement is a critical design parameter for mobile computing devices. Maximizing battery lifetime is a particularly difficult problem due to the non-linearity of the battery behavior and its dependence on the characteristics of the discharge profile. In this paper we address the problem of dynamic task scheduling with voltage scaling in a batterypowered DVS system. The objective is to maximize the battery performance measured in terms of charge consumption during execution of the tasks. We present a new battery-aware dynamic task scheduling algorithm, darEDF, based on an efficient slack utilization scheme that employs dynamic speed setting of tasks in run queue. We compare darEDF with three state of the art energy-efficient algorithms, lpfpsEDF, lppsEDF, lpSEH, with respect to battery performance and energy consumption. We show that darEDF has better performance than lpSEH (which has close to optimal energy value), and has lower run-time complexity.

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This article presents our work on the development of a fuel cell (FC) and battery hybrid (FC-Bh) system for use in portable microelectronic systems. We describe the design and control of the hybrid system, as well as a dynamic power management (DPM)-based energy management policy that extends its operational lifetime. The FC is of the proton exchange membrane (PEM) type, operates at room temperature, and has an energy density which is 4-6 times that of a Li-ion battery. The FC cannot respond to sudden changes in the load, and so a system powered solely by the FC is not economical. An FC-Bh power source, on the other hand, can provide the high energy density of the FC and the high power density of a battery.In this work we first describe the prototype FC-Bh system that we have built. Such a prototype helps to characterize the performance of a hybrid power source, and also helps explore new energy management strategies for embedded systems powered by hybrid sources. Next we describe a Matlab/Simulink-based FC-Bh system simulator which serves as an alternate experimental platform and that enables quick evaluation of system-level control policies. Finally, we present an optimization framework that explicitly considers the characteristics of the FC-Bh system and is aimed at minimizing the fuel consumption. This optimization framework is applied on top of a prediction-based DPM policy and is used to derive a new fuel-efficient DPM scheme. The proposed scheme demonstrates up to 32% system lifetime extension compared to a competing scheme when run on a real trace-based MPEG encoding example.