A five-fold increase in leakage current is predicted with each technology generation. While Dynamic Voltage Scaling (DVS) is known to reduce dynamic power consumption, it also causes increased leakage energy drain by lengthening the interval over which a computation is carried out. Therefore, for minimization of the total energy, one needs to determine an operating point, called the critical speed. We compute processor slowdown factors based on the critical speed for energy minimization. Procrastination scheduling attempts to maximize the duration of idle intervals by keeping the processor in a sleep/shutdown state even if there are pending tasks, within the constraints imposed by performance requirements. Our simulation experiments show that the critical speed slowdown results in up to 5% energy gains over a leakage oblivious dynamic voltage scaling. Procrastination scheduling scheme extends the sleep intervals to up to 5 times, resulting in up to an additional 18% energy gains, while meeting all timing requirements.

This paper examines two di erent mechanisms for saving power in battery-operated embedded systems. The rst is that the system can be placed in a sleep state if it is idle. However, a xed amount of energy is required to bring the system back i n to an active state in which it can resume work. The second way i n w h i c h p o wer savings can be achieved is by v arying the speed at which jobs are run. We utilize a power consumption curve P (s) w h i c h indicates the power consumption level given a particular speed. We assume that P (s) i s c o n vex, non-decreasing and non-negative f o r s 0. The problem is to schedule arriving jobs in a way that minimizes total energy use and so that each job is completed after its release time and before its deadline. We assume that all jobs can be preempted and resumed at no cost. Although each problem has been considered separately, this is the rst theoretical analysis of systems that can use both mechanisms. We g i v e an o ine algorithm that is within a factor of two of the optimal algorithm. We a l s o g i v e an online algorithm with a constant competitive ratio.

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Online dynamic power management (DPM) strategies refer to strategies that attempt to make power-mode-related decisions based on information available at runtime. In making such decisions, these strategies do not depend upon information of future behavior of the system, or any a priori knowledge of the input characteristics. In this paper, we present online strategies, and evaluate them based on a measure called the competitive ratio that enables a quantitative analysis of the performance of online strategies. All earlier approaches (online or predictive) have been limited to systems with two power-saving states (e.g., idle and shutdown). The only earlier approaches that handled multiple power-saving states were based on stochastic optimization. This paper provides a theoretical basis for the analysis of DPM strategies for systems with multiple power-down states, without resorting to such complex approaches. We show how a relatively simple "online learning" scheme can be used to improve the competitive ratio over deterministic strategies using the notion of "probability-based" online DPM strategies. Experimental results show that the algorithm presented here attains the best competitive ratio in comparison with other known predictive DPM algorithms. The other algorithms that come close to matching its performance in power suffer at least an additional 40% wake-up latency on average. Meanwhile, the algorithms that have comparable latency to our methods use at least 25% more power on average.