Hard real-time scheduling for low-energy using stochastic data and DVS processors

Gruian, Flavius

doi:10.1145/383082.383092

Cited by 130 publications

(55 citation statements)

References 30 publications

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“…The problem is formulated using mixed integer linear programming (MILP) as follows. Minimize (12) subject to…”

Section: A Offline Algorithmmentioning

confidence: 99%

“…The number of clock cycles has to be an integer and hence is restricted to the integer domain (20). The total energy consumption to be minimized, expressed by the objective in (12), is given by two sums. The inner sum indicates the energy dissipated by an individual task , depending on the time spent in each mode , while the outer sum adds up the energy of all tasks.…”

Section: A Offline Algorithmmentioning

confidence: 99%

“…The techniques presented in [12]- [15] use a stochastic approach to minimize the average-case energy consumption in hard real-time systems. The execution pattern is given as a probability distribution, reflecting the chance that a task execution can finish after a certain number of clock cycles.…”

mentioning

confidence: 99%

“…The execution pattern is given as a probability distribution, reflecting the chance that a task execution can finish after a certain number of clock cycles. In [12], [14], and [15], solutions were proposed that can be applied to singletask systems. In [13], the problem formulation was extended to multiple tasks, but it was assumed that continuous voltages were available on the processors.…”

mentioning

confidence: 99%

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Quasi-Static Voltage Scaling for Energy Minimization With Time Constraints

Alexandru

Eles

Jovanovic

et al. 2011

IEEE Trans. VLSI Syst.

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“…The problem is formulated using mixed integer linear programming (MILP) as follows. Minimize (12) subject to…”

Section: A Offline Algorithmmentioning

confidence: 99%

Section: A Offline Algorithmmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Quasi-Static Voltage Scaling for Energy Minimization With Time Constraints

Alexandru

Eles

Jovanovic

et al. 2011

IEEE Trans. VLSI Syst.

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“…The idea is to identify the program regions in which the CPU is mostly idle due to memory stalls, and slow them down for energy reduction. If the system architecture supports the overlapped execution of the CPU and memory operations, such a CPU slow-down will not result in a serious Stochastic IntraDVS uses the stochastic information on the program's execution time [17,31]. This technique is motivated by the idea that, from the energy consumption perspective, it is usually better to "start at low speed and accelerate execution later when needed" than to "start at high speed and reduce the speed later when the slack time is found" in the program execution.…”

Section: Other Intradvs Techniquesmentioning

confidence: 99%

Power-Aware Resource Management Techniques for Low-Power Embedded Systems

Kim¹,

Rosing²

2007

Chapman &Amp; Hall/CRC Computer &Amp; Information Science Series

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For example, consider a task with a deadline of 25 msec, running on a processor with the 50 MHz clock speed and 5.0 V supply voltage. If the task requires 5 • 10 5 cycles for its execution, the processor executes the task in 10 msec and becomes idle for the remaining 15 msec. (We call this type of an idle interval the slack time.) However, if the clock speed and the supply voltage are lowered to 20 MHz and 2.0 V, it finishes the task at its deadline (= 25 msec), resulting in 84% energy reduction. Since lowering the supply voltage also decreases the maximum achievable clock speed [43], various DVS algorithms for real-time systems have the goal of reducing supply voltage dynamically to the lowest possible level while satisfying the tasks' timing constraints. For real-time systems where timing constraints must be strictly satisfied, a fundamental energy-delay tradeoff makes it more challenging to dynamically adjust the supply voltage so that the energy consumption is minimized while not violating the timing requirements. In this chapter, we focus on DVS algorithms for hard real-time systems. On the other hand, dynamic power management decreases the energy consumption by selectively placing idle components into lower power states. At the minimum, the device needs to stay in the low-power state for long enough (defined as the break even time) to recuperate the cost of transitioning in and out of the state. The break even time T BE , as defined in Equation 1.1, is a function of the power consumption in the active state, P on , the amount of power consumed in the low power state, P sleep , and the cost of transition in terms of both time, T tr , and power, P pr. If it was possible to predict ahead of time the exact length of each idle period, then the ideal power management policy would place a device in the sleep state only when idle period will be longer than the break even time. Unfortunately, in most real systems such perfect prediction of idle period is not possible. As a result, one of the primary tasks DPM algorithms have is to predict when the idle period will be long enough to amortize the cost of transition to a low power state, and to select the state to transition to. Three classes of policies can be defined-timeout based, predictive and stochastic. The policies in each class differ in the way prediction of the length of the idle period is made, and the timing of the actual transition into the low power state (e.g., transitioning immediately at the start of an idle period vs. after some amount of idle time has passed).

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Power-Aware Communication Systems

Srivastava

2002

Power Aware Design Methodologies

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Battery-operated systems usually operate as part of larger networks where they wirelessly communicate with other systems. Conventional techniques for lowpower design, with their focus on circuits and computation logic in a system, are at best inadequate for such networked systems. The reason is two fold. First, the energy cost associated with wireless communications dominates the energy cost associated with computation. Being dictated primarily by a totally different set of laws (Shannon and Maxwell), communication energy, unlike computation energy, does not even benefit much from Moore's Law. Second, designers are interested in network-wide energy-related metrics, such as network lifetime, which techniques focused on computation at a single system cannot address. Therefore, in order to go beyond low-power techniques developed for stand-alone computing systems, this chapter describes communication-related sources of power consumption and network-level power-reduction and energy-management techniques in the context of wirelessly networked systems such as wireless multimedia and wireless sensor networks. General principles behind power-aware protocols and resource management techniques at various layers of networked systems -physical, link, medium access, routing, transport, and application -are presented. Unlike their conventional counterparts that only manage bandwidth to achieve performance, power-aware network protocols also manage the energy resource. Their goal is not just a reduction in the total power consumption. Rather, power-aware protocols seek a trade-off between energy and performance via network-wide power management to provide the right power at the right place and the right time.

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Hard real-time scheduling for low-energy using stochastic data and DVS processors

Cited by 130 publications

References 30 publications

Quasi-Static Voltage Scaling for Energy Minimization With Time Constraints

Quasi-Static Voltage Scaling for Energy Minimization With Time Constraints

Power-Aware Resource Management Techniques for Low-Power Embedded Systems

Power-Aware Communication Systems

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