Genetic Algorithm Based Idle Length Prediction Scheme for Dynamic Power Management
Fei Kong, Pin Tao, Shi-Qiang Yang, XiaoLi Zhao · 2006
Reducing energy consumption has become one of the most important challenges in designing computing systems. 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 periods. The more accurate the prediction is, the more efficient the policy is. This paper proposes a novel idea of using genetic algorithm to predict lengths of future idle periods. We take K adjacent idle periods and active periods as a load-gene and define some kinds of relationships between adjacent load-genes, then use genetic algorithm to predict future load-genes that most accords with the relationships. Experimental results show that the proposed scheme is more efficient than the exponential-average approach