Adaptive probability-based power management strategies
Dynamic Power Management (DPM) is an important technique to reduce power consumption in embedded and portable systems. Power hungry devices such as disk drives, network interfaces, and other peripheral devices are often designed with multiple power saving states, and control knobs are provided for changing their power states under the operating system control. DPM strategies are "online" strategies since they must make decisions about the timing of transitioning to lower power consumption states during idle periods without knowing when the next request for service will arrive. In this paper, we present a novel approach to designing adaptive online DPM strategies. This approach dynamically learns the probability distribution of idle period lengths from recent request patterns. A probability-based scheme then uses this information to optimize power saving actions. We present experimental results comparing our strategy with various strategies in the literature, including our previous work. Our study includes measuring power usage as well as the additional latency introduced from the delay in powering back up when a new request for service arrives. Our strategy exhibits the lowest power consumption among all the online algorithms. The other algorithms which come close to matching its performance in power all suffer at least an additional 40% latency on average. Meanwhile, the algo-rithms which have comparable latency to our method all use at least 25% more power on average. Thus, our probability-based DPM strategy is the most successful algorithm in balancing power usage as well as latency incurred.