Dynamic Power Management Policy Using Genetic Algorithm to Predict Idle Periods
Yang Meng Lin Shi · Computer Engineering and Applications Journal · 2006
Reducing energy consumption has become one of the most important challenges in designing computing sys- tems.Dynamic power management policies exploit components’idle periods to save energy.If one idle period of some component is long enough, the component can be put into low power state during this period in order to reduce energy consumption.Many dynamic power management policies are based on predicting lengths of components’future idle peri- ods.The more accurate the prediction is, the more efficient the policy is.This paper proposes a novel idea of using ge- netic algorithm to predict lengths of future idle periods.We take K pairs of adjacent idle periods and active periods as a load- gene and define a kind of relationship between adjacent load- genes, and use genetic algorithm to predict future load- genes that most accords with the relationship, then the future idle periods’lengths can be computed with the pre- dicted load- genes.Experimental results show that the proposed policy has more accurate prediction in comparison with exponential- average approach, thus this policy can be used for dynamic power management more efficiently.