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Reducing power consumption of computing devices by forecasting computing performance needs

机译:通过预测计算性能需求来减少计算设备的功耗

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.
机译:提供了以下技术和系统,这些技术和系统通过建立和使用模型来预测和降低计算设备的能耗,这些模型基于观察来预测计算系统的一个或多个组件的未来工作,并在权衡决策的决策分析中使用此类预测将组件过渡到较低功耗和性能状态的成本和收益。可以通过机器学习方法从收集的有关当前和最近观察到的组件未来性能要求的数据库中生成预测模型。这些模型可以用于根据提示来预测计算设备的未来性能需求。在各个方面中,用于预测性能需求的模型(考虑了用户的延迟偏好和容忍度)用于指导决策的成本效益分析。

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