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A gradient learning optimization for dynamic power management
© 2015 IEEE. Dynamic power management (DPM) is a power dissipation reduction technology aimed to adapting the power and performance of a system to its workload. In this paper, we propose a gradient learning optimization method for the DPM problem. Our method does not depend on accurate model parameters and is only based on a single sample path of system. Thus, there is no any transition probability to be calculated. Moreover, the new method only need less storage for the performance optimization. Simulation results demonstrate the applicability of the proposed method.
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Event
Systems, Man, and Cybernetics. Conference (2015 : Hong Kong)Pagination
2061 - 2066Publisher
IEEELocation
Hong KongPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2015-10-09End date
2015-10-12ISBN-13
9781479986965Language
engPublication classification
E1.1 Full written paper - refereedTitle of proceedings
SMC 2015 : Proceedings of the 2015 IEEE International Conference on Systems, Man, and CyberneticsUsage metrics
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