Abstract
Dynamic power management is a technique used to save power when the system is idle. Earlier it was assumed that the prediction can be done only in long range dependent systems. But a single user will not work similarly the next time, so a single assumption will not hold good. To overcome the above assumptions, we propose an Elman Model which uses Moving Average, Elman Backprop network and random walk model to predict the idle period. Here we use Artificial Neural Network (ANN) in which we train the neurons in a particular way the user desires, replacing neurons by time series we can calculate how much power is saved. This model utilizes both long range dependency and central tendency to predict the past idle periods, by which we predict the future idle period. By simulation we can show that this method achieves higher power saving compared to other methods.
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Rajasekaran, P., Prabakaran, R., Thanigaiselvan, R. (2011). Implementation of Elman Backprop for Dynamic Power Management. In: Balasubramaniam, P. (eds) Control, Computation and Information Systems. ICLICC 2011. Communications in Computer and Information Science, vol 140. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19263-0_28
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DOI: https://doi.org/10.1007/978-3-642-19263-0_28
Publisher Name: Springer, Berlin, Heidelberg
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