| 摘要 |
Techniques and systems are provided that work to minimize the energy usage of computing devices by building and using models that predict the future work required of one or more components of a computing system, based on observations, and using such forecasts in a decision analysis that weighs the costs and benefits of transitioning components to a lower power and performance state. Predictive models can be generated by machine learning methods from libraries of data collected about the future performance requirements on components, given current and recent observations. The models may be used to predict in an ongoing manner the future performance requirements of a computing device from cues. In various aspects, models that predict performance requirements that take into consideration the latency preferences and tolerances of users are used in cost-benefit analyses that guide powering decisions. |
