Dynamic power management using adaptive learning tree

Dynamic power management (DPM) is a technique to reduce the power consumption of electronic systems by selectively shutting down idle components. The quality of the shutdown control algorithm (the power management policy) mostly depends on knowledge of the user's behavior, which in many cases is initially unknown or non-stationary. For this reason, DPM policies should be capable of adapting to changes in user behavior. In this paper, we present a novel DPM scheme based on idle period clustering and adaptive learning trees. We also provide a design guide for applying our technique to components with multiple sleep states. Experimental results show that our technique outperforms other advanced DPM schemes as well as simple time-out policies. The proposed approach shows little deviation of efficiency for various workloads having different characteristics, while other policies show that their efficiency changes drastically depending on the trace data characteristics. Furthermore, experimental evidence indicates that our workload learning algorithm is stable and has fast convergence.