Algorithmic Support for Power Management

电源管理的算法支持

• 批准号: 0830558
• 负责人: Kirk Pruhs
• 金额: $ 29.99万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-08-01 至 2012-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0830558&HistoricalAwards=false
• 关键词: Algorithmic; Support; Power; Management

Power is now widely recognized as a first class architectural design constraint at all levels of computing, from the level of processor chips to the level of data centers. There are at least three distinct common goals for power management: energy conservation, temperature management, and limiting maximum power. The most commonly used power management technique is speed scaling, which involves changing the speed of the processor. Essentially all currently produced general purpose processors may be run at multiple speeds, and all leading processor manufacturers produce associated software that manages power by scaling processor speed. However, speed scaling as a power management technique, has to date mostly been applied in a reactive fashion in that it is usually only invoked in response to abnormal conditions, and its application is largely decoupled from the scheduling policies in that they are working independently of each other. It is widely accepted that integrated proactive power management and scheduling policies will result in better power management than simple reactive strategies.The proposed research investigates algorithmic problems related to integrated power management and and scheduling. The resulting optimization problems have dual objectives, since one seeks to optimize both some quality-of-service measure of the schedule, and some power related criteria. In general these objectives are conflicting in that the more power that one uses, the better quality of service that can be provided. The algorithmic solutions to these problem then involve increasing power when the improvement in the scheduling objective justifies the increased cost in the power management objective. The goals of the research are threefold. The first goal is to develop algorithms that could form the basis for future power management software. The second goal is to build a toolkit of widely applicable algorithmic methods for power management problems. The third goal is help increase the computing community's ability to abstractly reason about power, energy and temperature.

从处理器芯片到数据中心，功率现在被广泛认为是所有计算级别的一流架构设计约束。电源管理至少有三个不同的共同目标：节能、温度管理和限制最大功率。最常用的电源管理技术是速度缩放，它涉及到改变处理器的速度。基本上，目前生产的所有通用处理器都可以以多种速度运行，所有领先的处理器制造商都生产相关的软件，通过缩放处理器速度来管理电源。然而，速度扩展作为一种电源管理技术，迄今为止主要以响应方式应用，因为它通常仅在响应异常情况时调用，并且它的应用在很大程度上与调度策略解耦，因为它们彼此独立工作。人们普遍认为，集成的主动电源管理和调度策略将比简单的被动策略产生更好的电源管理。本研究主要探讨与综合电源管理及调度相关的算法问题。由此产生的优化问题具有双重目标，因为一方面寻求优化调度的某些服务质量度量，另一方面寻求优化一些与功率相关的标准。一般来说，这些目标是相互冲突的，因为一个人使用的权力越大，所能提供的服务质量就越好。当调度目标的改进证明了功率管理目标的成本增加时，这些问题的算法解决方案就涉及到功率的增加。这项研究的目标有三个。第一个目标是开发算法，为未来的电源管理软件奠定基础。第二个目标是为电源管理问题建立一个广泛适用的算法方法工具包。第三个目标是帮助提高计算社区对电力、能源和温度进行抽象推理的能力。