Optimal dynamic voltage scaling for wireless sensor nodes with real-time constraints

Cassandras, Christos G.; Zhuang, Shixin

doi:10.1117/12.629893

Cited by 5 publications

(2 citation statements)

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“…For instance, in manufacturing systems the cost of operating a machine is monotonically decreasing and convex in the processing time of a part (Pepyne and Cassandras 2000); in embedded devices, the processing time of a task is a convex monotonically decreasing function of the voltage applied to its CPU and the energy expended is monotonically decreasing and convex in the processing time of a task Cassandras and Zhuang 2005). For a typical CMOS microprocessor, an extensive discussion found in points to the fact that the interval over which the processor speed is controlled possesses these properties.…”

Section: Problem Formulationmentioning

confidence: 99%

“…2 is a DES with soft constraints where these inequalities are removed and the cost function above consists of two parts (Cassandras and Zhuang 2005): a cost on the resource which is increasing in the processing time, and a cost imposed on tasks that violate the constraint x i ≤ d i . This is appropriate for applications which can tolerate occasional deadline misses of real-time tasks.…”

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confidence: 99%

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Optimal Control of Discrete Event Systems with Weakly Hard Real-Time Constraints

Zhuang

Cassandras

2008

Discrete Event Dyn Syst

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We consider Discrete Event Systems that can dynamically allocate resources in order to process tasks with real-time constraints. In the case of "weakly hard" constraints, a fraction of tasks is allowed to violate them, as long as m out of any k consecutive tasks meet their respective constraints. This is a generalization of a system with purely hard real-time constraints where m = k = 1. For non-preemptive and aperiodic tasks, we formulate an optimization problem where task processing times are controlled so as to minimize a cost function while guaranteeing that a "weakly hard" criterion is satisfied. We establish a number of structural properties of the solution to this problem which lead to an efficient algorithm that does not require any explicit nonlinear programming problem solver. The low complexity of this algorithm makes it suitable for on-line applications. Simulation examples illustrate the performance improvements in such optimally controlled systems compared to ad hoc schemes.

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Section: Problem Formulationmentioning

confidence: 99%

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confidence: 99%