公开(公告)号：US20100332876A1

公开(公告)日：2010-12-30

申请号：US12493058

申请日：2009-06-26

Applicant: Mahlon David Fields, JR. ,  Eric J. Horvitz

Inventor： Mahlon David Fields, JR. ,  Eric J. Horvitz

IPC: G06F1/32

CPC classification number: G06F1/3203 ,  G06F9/5094 ,  Y02D10/22

Abstract: 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.

Abstract translation: 提供了技术和系统，其工作是通过构建和使用基于观察来预测计算系统的一个或多个组件的未来工作的模型并且在重量的决策分析中使用这样的预测来最小化计算设备的能量使用 将组件转换到较低的功率和性能状态的成本和收益。 预测模型可以通过机器学习方法从收集的数据库中获取，关于组件的未来性能要求，给出当前和最近的观察结果。 这些模型可以用于以持续的方式预测来自线索的计算设备的未来性能要求。 在各个方面，预测考虑延迟偏好和用户容差的性能要求的模型被用于指导供电决策的成本效益分析。

公开(公告)号：US08190939B2

公开(公告)日：2012-05-29

申请号：US12493058

申请日：2009-06-26

Applicant: Mahlon David Fields, Jr. ,  Eric J. Horvitz

Inventor： Mahlon David Fields, Jr. ,  Eric J. Horvitz

IPC: G06F1/32

CPC classification number: G06F1/3203 ,  G06F9/5094 ,  Y02D10/22

Abstract: 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.

Abstract translation: 提供了技术和系统，其工作是通过构建和使用基于观察来预测计算系统的一个或多个组件的未来工作的模型并且在重量的决策分析中使用这样的预测来最小化计算设备的能量使用 将组件转换到较低的功率和性能状态的成本和收益。 预测模型可以通过机器学习方法从收集的数据库中获取，关于组件的未来性能要求，给出当前和最近的观察结果。 这些模型可以用于以持续的方式预测来自线索的计算设备的未来性能要求。 在各个方面，预测考虑延迟偏好和用户容差的性能要求的模型被用于指导供电决策的成本效益分析。

公开(公告)号：US06496912B1

公开(公告)日：2002-12-17

申请号：US09276271

申请日：1999-03-25

Applicant: Mahlon David Fields, Jr. ,  Stuart Sechrest

Inventor： Mahlon David Fields, Jr. ,  Stuart Sechrest

IPC: G06F1200

CPC classification number: G06F12/124 ,  G06F12/121

Abstract: A computer system, method and computer readable medium for memory management with intelligent trimming of pages of working sets are disclosed. The computer system has memory space allocatable in chunks, known as pages, to specific application programs or processes. The pages allocated to a specific application program or process define a working set of pages for the program or process. Occasionally, a system runs short of free memory space and needs to reduce the size of working sets using a process called trimming. A trimming method is disclosed that estimates numbers of trimmable pages for working sets based upon a measure of how much time has elapsed since the memory pages were last accessed by the corresponding application program. This estimation is performed prior to the need to trim working sets, and the trimming method uses these estimates to facilitate faster and more accurate trimming and thus faster recovery from shortages of free memory.

Abstract translation: 公开了一种用于存储器管理的计算机系统，方法和计算机可读介质，其具有工作集页面的智能修整。 计算机系统具有可分批的存储空间，称为页面，用于特定应用程序或进程。 分配给特定应用程序或进程的页面为程序或进程定义了一组工作页面。 偶尔，系统缺少可用内存空间，需要使用称为修剪的进程来减小工作集的大小。 公开了一种修整方法，其基于从相应的应用程序上次访问存储器页面以来经过了多少时间的量度来估计工作集的可修剪页数。 该估计在需要修整工作集之前进行，并且修剪方法使用这些估计来促进更快和更准确的修剪，从而更快地从可用存储器的缺乏恢复。