Predictive Power Conservation

预测节能

• 批准号: 9704348
• 负责人: Darrell Long
• 金额: $ 12.02万
• 依托单位: University of California-Santa Cruz
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-09-15 至 2000-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9704348&HistoricalAwards=false
• 关键词: Predictive; Power; Conservation

The goal of this project is to investigate the use of adaptive techniques to minimize the power consumed by peripherals in mobile computing systems. Since battery life is one of the most critical resources in the mobile computing environment, good energy conservation methods can dramatically increase the utility of mobile systems. The investigators have previously demonstrated that new adaptive techniques from the machine learning community are very effective in solving disk spin-down problem (deciding when to shut down the disk in order to maximize power savings) on traces of disk activity. The project will continue this work in two directions. The first direction is to compare the performance of the new techniques with that of other adaptive algorithms for the spin-down problem. The second direction involves applying similar machine learning methods to wireless modems (radios). In addition to the power savings gained by turning off the radio when the network interface is not needed, adaptively choosing the broadcast power level may also lead to significant energy savings.

这个项目的目标是研究使用自适应技术来最小化移动的计算系统中外围设备的功耗。由于电池寿命是移动的计算环境中最关键的资源之一，因此良好的节能方法可以显著增加移动的系统的效用。研究人员之前已经证明，机器学习社区的新自适应技术在解决磁盘活动痕迹上的磁盘旋转问题（决定何时关闭磁盘以最大限度地节省电力）方面非常有效。 该项目将从两个方向继续开展这项工作。第一个方向是比较新技术的性能与其他自适应算法的降速问题。 第二个方向涉及将类似的机器学习方法应用于无线调制解调器（无线电）。除了通过在不需要网络接口时关闭无线电而获得的功率节省之外，自适应地选择广播功率电平还可以导致显著的能量节省。