In this paper, we address power-aware scheduling of periodic hard real-time tasks using dynamic voltage scaling. Our solution includes three parts: (a) a static (off-line) solution to compute the optimal speed, assuming worst-case workload for each arrival, (b) an on-line speed reduction mechanism to reclaim energy by adapting to the actual workload, and (c) an online, adaptive and speculative speed adjustment mechanism to anticipate early completions of future executions by using the average-case workload information. All these solutions still guarantee that all deadlines are met. Our simulation results show that the reclaiming algorithm saves a striking 50% of the energy over the static algorithm. Further, our speculative techniques allow for an additional approximately 20% savings over the reclaiming algorithm. In this study, we also establish that solving an instance of the static power-aware scheduling problem is equivalent to solving an instance of the rewardbased scheduling problem [1, 4] with concave reward functions. 1

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Power eficient design of real-time embedded systems based on programmable processors becomes more important as system functionality is increasingly realized through software. This pa-perpresents a power optimization method for real-time embedded applications on a variable speed processor: The method com-bines off-line and on-line components. The off-line component determines the lowest possible maximum processor speed while guaranteeing deadlines of all tasks. The on-line component dy-namically varies the processor speed or bring a processor into a power-down mode according to the status of task set in order to exploit execution time variations and idle intervals. Experimen-tal results show that the proposed method obtains a signijicant power reduction across several kinds of applications. 1

Combining high performance with low power consumption is becoming one of the primary objectives of processor designs. Instead of relying just on sleep mode for conserving power, an increasing number of processors take advantage of the fact that reducing the clock frequency and corresponding operating voltage of the CPU can yield quadratic decrease in energy use. However, performance reduction can only be beneficial if it is done transparently, without causing the software to miss its deadlines. In this paper, we describe the implementation and performance-setting algorithms used in

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Energy consumption has become an increasingly important consideration in designing many real-time embedded systems. Variable voltage processors, if used properly, can dramatically reduce such system energy consumption. In this paper, we present a technique to determine voltage settings for a variable voltage processor that utilizes a fixed priority assignment to schedule jobs. Our approach also produces the minimum constant voltage needed to feasibly schedule the entire job set. Our algorithms lead to significant energy saving compared with previously presented approaches.

Dynamic voltage scaling (DVS), which adjusts the clock speed and supply voltage dynamically, is an effective technique in reducing the energy consumption of embedded realtime systems. The energy efficiency of a DVS algorithm largely depends on the performance of the slack estimation method used in it. In this paper, we propose a novel DVS algorithm for periodic hard real-time tasks based on an improved slack estimation algorithm. Unlike the existing techniques, the proposed method takes full advantage of the periodic characteristics of the real-time tasks under priority-driven scheduling such as EDF. Experimental results show that the proposed algorithm reduces the energy consumption by 20#40% over the existing DVS algorithm. The experiment results also show that our algorithm based on the improved slack estimation method gives comparable energy savings to the DVS algorithm based on the theoretically optimal (but impractical) slack estimation method.

This paper addresses battery-aware static scheduling in batterypowered distributed real-time embedded systems. As suggested by previous work, reducing the discharge current level and shaping its distribution are essential for extending the battery lifespan. We propose two battery-aware static scheduling schemes. The first one optimizes the discharge power profile in order to maximize the utilization of the battery capacity. The second one targets distributed systems composed of voltage-scalable processing elements (PEs). It performs variable-voltage scheduling via efficient slack time re-allocation, which helps reduce the average discharge power consumption as well as flatten the discharge power profile. Both schemes guarantee the hard real-time constraints and precedence relationships in the real-time distributed embedded system specification. Based on previous work, we develop a battery lifespan evaluation metric which is aware of the shape of the discharge power profile. Our experimental results show that the battery lifespan can be increased by up to 29% by optimizing the discharge power file alone. Our variable-voltage scheme increases the battery lifespan by up to 76% over the non-voltage-scalable scheme and by up to 56% over the variable-voltage scheme without slack-time reallocation. 1.

In this paper, we provide an efficient solution for periodic real-time tasks with (potentially) different power consumption characteristics. We show that a task T i can run at a constant speed S i at every instance without hurting optimality. We sketch an O(n log n) algorithm to compute the optimal S i values. We also prove that the EDF (Earliest Deadline First) scheduling policy can be used to obtain a feasible schedule with these optimal speed values.

In this paper , we present a power-conscious algorithm for jointly scheduling multi-rate periodic task graphs and aperiodic tasks in distributed real-time embedded systems. While the periodic task graphs have hard deadlines, the aperiodic tasks can have either hard or soft deadlines. Periodic task graphs are first scheduled statically. Slots are created in this static schedule to accommodate hard aperiodic tasks. Soft aperiodic tasks are scheduled dynamically with an on-line scheduler. Flexibility is introduced into the static schedule and optimized to allow the on-line scheduler to make dynamic modifications to the static schedule. This helps minimize the response times of soft aperiodic tasks through both resource reclaiming and slack stealing. Of course, the validity of the static schedule is maintained. The on-line scheduler also employs dynamic voltage scaling and power management to obtain a power-efficient schedule. Experimental results show that the flexibility introduced into the static schedule helps improve the response times of soft aperiodic tasks by up to 43%. Dynamic voltage scaling and power management reduce power by up to 68%. The scheme in which the static schedule is allowed to be flexible achieves up to 32% more power saving compared to the scheme in which no flexibility is allowed, when both schemes are power-conscious. Our work gives an average architecture price saving of 30% over a previous approach for embedded system architectures synthesized with execution slots for hard aperiodic tasks present. 1.