Collaborative Research: Algorithmic Support for Power Aware Computing and Communication

协作研究：功耗感知计算和通信的算法支持

• 批准号: 0514082
• 负责人: Sandra Irani
• 金额: $ 15万
• 依托单位: University of California-Irvine
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-07-15 至 2009-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0514082&HistoricalAwards=false
• 关键词: Collaborative; Research; Algorithmic; Support; Power

Intellectual Merit: The power consumption rate of computing devices has been increasing exponentially. This makes it increasingly difficult to supply energy to devices and to cool these devices. Poweraware computation is especially important in the domain of sensor networks which are composed of small battery-powered nodes. Power management in sensor networks is viewed as so critical that it must be dealt with at all layers of the protocol stack.Many power management techniques have been proposed and implemented. Most of these techniques are similar in that they reduce or eliminate power to some or all components of the device. However, there is an inherent conflict between power management and performance; in general, the more power that is available, the better the performance that can be achieved. As a result, it is generally proposed that power reduction techniques be preferentially applied during times when performance is lesscritical. However, this requires a policy to determine how essential performance is at any given time and how to apply a particular power reduction technique. For example, to use the frequency scaling technique, where the speed of the clock is changed dynamically, one needs a policy to set the speed at each point in time. There is a growing consensus that these policies must incorporate information provided by applications and high levels of the operating system, and that current tools and mechanisms for power management are inadequate and require more research. The authors propose to formalize powermanagement problems as optimization problems, and then develop algorithms that are optimal by these criteria. The goal of this research is to develop effective algorithms for specific problems within the domain of power management, as well as to build a toolkit of widely applicable algorithmic methods for problems that arise in energy-bounded and temperature-bounded computation. The authors propose to initially focus on problems that deal with speed scaling and power-down techniques, since these are currently the dominant techniques in practice.Broader Impacts: The authors propose to both develop fundamental theoretical techniques, and to apply these techniques to attack timely and important applications in computer systems. Both PIs have an established track record of working closely with researchers in applied areas to ensure that the theoretical models developed match the associated real-world problems. This is essential for theoretical results to have an impact. This work will continue to foster this very productive cross-fertilization betweenthese experimental systems researchers and theoretical computer science. The students supported under this grant will be influenced by this philosophy of research. They will be trained to be proactive in working with researchers in applied domains to bring important and interesting problems into the theory community. They will also be encouraged to publish the resulting work in systems as well as theory conferences to ensure that new algorithmic discoveries have an impact. As part of this project, the authors also plan to continue outreach work to high schools students, encouraging underrepresented groups to choose careers in technology related fields. They have developed a talk outlining diverse opportunities within computer science and plan to involve graduate and undergraduate students in presenting this talk at local high schools. The work in this proposal is featured in the talk. In addition, they plan to involve students from underrepresented groups in research projects related to power management.

智能优点：计算设备的耗电量一直在呈指数级增长。这使得向设备供应能量和冷却这些设备变得越来越困难。在由电池供电的小型节点组成的传感器网络领域，能量感知计算尤为重要。传感器网络中的功率管理被认为是非常关键的，因此必须在协议栈的各个层进行处理。这些技术中的大多数都是相似的，因为它们减少或消除了设备的一些或所有组件的功率。但是，电源管理和性能之间存在内在冲突；一般来说，可用的电源越多，可以实现的性能就越好。因此，通常建议在性能不那么关键的时候优先应用功率降低技术。然而，这需要一个策略来确定在任何给定时间性能的重要性，以及如何应用特定的功率降低技术。例如，要使用时钟速度动态变化的频率缩放技术，需要一个策略来设置每个时间点的速度。越来越多的人达成共识，认为这些政策必须纳入应用程序和操作系统高层提供的信息，目前的电源管理工具和机制不足，需要进行更多研究。作者建议将电源管理问题形式化为优化问题，然后开发符合这些准则的最优算法。这项研究的目标是为电源管理领域中的特定问题开发有效的算法，以及为能量受限和温度受限计算中出现的问题建立一个广泛适用的算法方法工具包。作者建议首先关注涉及速度调整和掉电技术的问题，因为这些技术目前在实践中占主导地位。广泛影响：作者建议既发展基本理论技术，又应用这些技术来攻击计算机系统中及时和重要的应用程序。这两家私人投资机构都有与应用领域的研究人员密切合作的既定记录，以确保开发的理论模型与相关的现实世界问题相匹配。这是理论结果产生影响的关键。这项工作将继续促进这些实验系统研究人员和理论计算机科学之间的这种非常富有成效的交叉受精。这笔助学金资助的学生将受到这种研究理念的影响。他们将接受培训，积极主动地与应用领域的研究人员合作，将重要而有趣的问题带入理论界。他们还将被鼓励在系统和理论会议上发表由此产生的工作，以确保新的算法发现产生影响。作为该项目的一部分，作者还计划继续向高中生推广工作，鼓励代表性不足的群体选择与技术相关的职业。他们已经制定了一个演讲，概述了计算机科学领域的各种机会，并计划让研究生和本科生参与到当地高中的演讲中。这项提案中的工作是演讲中的特色。此外，他们计划让代表人数不足的群体的学生参与与权力管理相关的研究项目。