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.

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.

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Abstract. Dynamic power management (DPM) refers to the use of runtime strategies in order to achieve a tradeoff between the performance and power consumption of a system and its components. We present an approach to analysing stochastic DPM strategies using probabilistic model checking as the formal framework. This is a novel application of probabilistic model checking to the area of system design. This approach allows us to obtain performance measures of strategies by automated analytical means without expensive simulations. Moreover, one can formally establish various probabilistically quantified properties pertaining to buffer sizes, delays, energy usage etc., for each derived strategy.

SystemC promises an environment for faster hardware/ software design-space exploration.

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Abstract-Probabilistic-model checking is a formal verification technique for analyzing the reliability and performance of systems exhibiting stochastic behavior. In this paper, we demonstrate the applicability of this approach and, in particular, the probabilistic-model-checking tool PRISM to the evaluation of reliability and redundancy of defect-tolerant systems in the field of computer-aided design. We illustrate the technique with an example due to von Neumann, namely NAND multiplexing. We show how, having constructed a model of a defect-tolerant system incorporating probabilistic assumptions about its defects, it is straightforward to compute a range of reliability measures and investigate how they are affected by slight variations in the behavior of the system. This allows a designer to evaluate, for example, the tradeoff between redundancy and reliability in the design. We also highlight errors in analytically computed reliability bounds, recently published for the same case study.

In the recent years, Phasor Measurement Unit (PMU) based Wide Area Measurement System (WAMS) has been receiving ever increasing attention from the academia as well as from the industry. Power utilities have been designing and implementing WAMS to provide more intelligent monitoring, control, and protection of the power grid. In order to achieve real-time operations in the modern power systems, construction of an economic and efficient communication infrastructure is a necessity, and various utilities have been laying fiber optical network along their transmission and distribution right of way to leverage the abilities of PMUs in providing greater visibility over a larger area of the grid -thereby providing opportunities for better control and stability. The choice of network architecture, protocols, and various measures for quality of service guarantees must be made by the network architects at the utilities. In this paper, we present a methodology based on profiling data traffic for various WAMS applications according to their communication requirements, and then creating simulation models and scenarios to obtain various parameters for specific architectural and protocol choices. Our simulation results are encouraging in the sense that under modest choices all the applications meet the timing and bandwidth requirements. However, the main contribution of this work is the methodology that would allow the utilities to evaluate various communication infrastructure choices while deploying WAMS.