This work is based on the observation that existing energy management techniques for mobile devices, such as dynamic voltage scaling (DVS), are non-cooperative in the sense that they reduce the energy consumption of a single device, disregarding potential consequences for other constraints (e.g., end-to- end deadlines) and/or other devices (e.g., energy consumption on neighboring devices). This paper argues that energy management in distributed real-time systems has to be end-to-end in nature, requiring a coordinated approach among communicating devices. A cooperative distributed energy management technique (Co-DVS) is proposed that: i) adapts and maintains end-to-end latencies within specified timeliness requirements (deadlines); and ii) enhances energy savings at the nodes with the highest pay-off factors that represent the relative benefits or significance of conserving energy at a node. The proposed technique employs a feedback-based approach to dynamically distribute end-to-end slack among the devices based on their pay-off factors.

Reducing energy consumption is an important consideration in embedded real-time system development. This work examines systems that contain a DVFS managed CPU executing packet producing tasks and a DPM-controlled network interface. We introduce a novel approach to minimize energy consumed by the network resource on such a system, through careful selection of voltage and frequency levels on the CPU. Contrary to existing claims which state that DVFS should not be employed when the CPU is not a significant consumer of energy, we show that our DVFS technique can reduce system energy by as much as 35%, even when the CPU energy consumption is negligible. Furthermore, we motivate the need to balance the CPU and network energy and present two techniques to do so. One is based on off-line analysis and the other is a conservative on-line approach. We then validate the proposed methods using both simulation and an implementation in the Linux kernel

Time transition overhead is a critical problem for hard real-time systems that employ dynamic voltage scaling (DVS) for power and energy management. While it is a common practice of much previous work to ignore transition overhead, these algorithms cannot guarantee deadlines and/or are less effective in saving energy when transition overhead is significant and not appropriately dealt with. In this article we introduce two techniques, one offline and one online, to correctly account for transition overhead in preemptive fixed-priority real-time systems. We present several DVS scheduling algorithms that implement these methods that can guarantee task deadlines under arbitrarily large transition time overheads and reduce energy consumption by as much as 40% when compared to previous methods.

Dynamic voltagescaling and sleep state control have been shown to be extremely effective in reducing energy consumption in CMOS circuits. Though plenty of research papers have studied the application of these techniques in real-time embedded system design through intelligent task and/or voltage scheduling, most of these results are limited to relatively simple real-time application models. In this paper, a comprehensive real-time application model including periodic, sporadic and bursty tasks as well as distributed real-time constraints such as end-to-end delays is considered. Two methods are presented for reducing energy consumption while satisfying complex real-time constraints for this model. Experimental results show that the methods achieve significant energy savings without violating any deadlines.

Until recently, most 3D graphics applications had been regarded as too computationally intensive for devices other than desktop computers and gaming consoles. This notion is rapidly changing due to improving screen resolutions and computing capabilities of mass-market handheld devices such as cellular phones and PDAs. As the mobile 3D gaming industry is poised to expand, significant innovations are required to provide users with high-quality 3D experience under limited processing, memory and energy budgets that are characteristic of the mobile domain.Energy saving schemes such as Dynamic Voltage and Frequency Scaling (DVFS), as well as system-level power and performance optimization methods for mobile devices require accurate and fast workload prediction. In this paper, we address the problem of workload prediction for mobile 3D graphics. We propose and describe a signature-based estimation technique for predicting 3D graphics workloads. By analyzing a gaming benchmark, we show that monitoring specific parameters of the 3D pipeline provides better prediction accuracy over conventional approaches. We describe how signatures capture such parameters concisely to make accurate workload predictions. Signature-based prediction is computationally efficient because first, signatures are compact, and second, they do not require elaborate model evaluations. Thus, they are amenable to efficient, real-time prediction. A fundamental difference between signatures and standard history-based predictors is that signatures capture previous outcomes as well as the cause that led to the outcome, and use both to predict future outcomes. We illustrate the utility of signature-based workload estimation technique by using it as a basis for DVFS in 3D graphics pipelines.

The world of 3D graphics, until recently restricted to high-end workstations and game consoles, is rapidly expanding into the domain of mobile platforms such as cellular phones and PDAs. Even as the mobile chip market is poised to exceed production of 500 million chips per year, incorporation of 3D graphics in handhelds poses several serious challenges to the hardware designer. Compared with other platforms, graphics on handhelds have to contend with limited energy supplies and lower computing horsepower. Nevertheless, images must still be rendered at high quality since handheld screens are typically held closer to the observer's eye, making imperfections and approximations very noticeable. In this paper, we provide an in-depth quantitative analysis of the power consumption of mobile 3D graphics pipelines. We analyze the effects of various 3D graphics factors such as resolution, frame rate, level of detail, lighting and texture maps on power consumption. We demonstrate that significant imbalance exists across the workloads of different graphics pipeline stages. In addition, we illustrate how this imbalance may vary dynamically, depending on the characteristics of the graphics application. Based on this observation, we identify and compare the benefits of candidate dynamic voltage and frequency scaling (DVFS) schemes for mobile 3D graphics pipelines. In our experiments we observe that DVFS for mobile 3D graphics reduces energy by as much as 50%

Jitter is a critical problem for the design of both distributed embedded systems and real-time control systems. This work considers meeting the completion jitter constraints of a set of independent, periodic, hard real-time tasks scheduled according to a preemptive fixed-priority scheme. Control over completion jitter is achieved by judiciously applying dynamic voltage scaling (DVS). Through simulation, the proposed method is shown to be an effective tool to meet jitter constraints on a variety of systems.

We present an online dynamic voltage scaling (DVS) algorithm for preemptive fixed-priority real-time systems called low power limited demand analysis with transition overhead (lpLDAT). It is the first algorithm in its class to explicitly account for transition overhead, and can reduce the energy consumption by as much as 40% when compared to previous methods.

While dynamic voltage scaling (DVS) is an efficient technique in reducing the dynamic energy consumption of a CMOS processor, methods that employ DVS without considering leakage current are quickly becoming less efficient when considering the processor's overall energy consumption. A leakage conscious DVS voltage schedule may require the processor to run at a higher-than-necessary speed to execute a given set of real-time tasks, which can result in a large number of idle intervals. To effectively reduce the energy consumption during these idle intervals, and therefore the overall energy consumption, the DVS schedule must judiciously allow the processor to enter and leave the power down state during these idle intervals, while considering the time and energy cost of doing so. In this paper, we present a scheduling technique that can effectively reduce the overall energy consumption for hard real-time systems scheduled according to a fixed priority (FP) scheme. Experimental results demonstrate that a processor using our strategy consumes as less as 15% of the idle energy of a processor employing the conventional strategy.

Voltage scheduling is an essential technique used to exploit the benefit of dynamic voltage-scaling processors. Though extensive research exists in this area, current processor limitations such as time and energy transition overhead and voltage-level discretization are often dismissed as insignificant. We show that for hard real-time applications, disregarding these details can lead to suboptimal or even invalid results. We propose two algorithms to account for these limitations. The first is a greedy approach, while the second is more complex, but can significantly reduce the system's energy consumption. Through experimental results on both real and randomly generated systems, we show the effectiveness of both algorithms and explore what conditions make it beneficial to use the complex algorithm over the basic one.

Voltage scheduling is indispensable for exploiting the benefit of variable voltage processors. Though extensive research has been done in this area, current processor limitations such as transition overhead and voltage level discretization are often considered insignificant and are typically ignored. We show that for hard, real-time applications, disregarding such details can lead to sub-optimal or even invalid results. We propose two algorithms that guarantee valid solutions. The first is a greedy yet simple approach, while the second is more complex but significantly reduces energy consumption under certain conditions. Through experimental results on both real and randomly generated systems, we show the effectiveness of both algorithms, and explore what conditions make it beneficial to use the complex algorithm over the basic one.