Collaborative Research: Autonomic Power and Performance Management in Distributed Computing Systems

本计画的目标是发展一个理论架构和一个阶层式的方法论，以透过（a）线上建模、监控和分析电力消耗和效能;（B）自适应学习和自动识别策略，以最大限度地减少功耗，同时保持广泛的工作负载和应用程序所需的服务质量要求;以及（c）根据所选择的优化策略来动态地重新配置计算、存储和网络资源。 所提出的方法利用新兴的硬件和软件标准来提高处理器和设备的功率效率，例如Intel快速启动和SpeedStep处理器、磁盘降速、功率感知页面分配和RDRAM，这些处理器和设备提供更广泛的低功耗状态并降低转换成本。该项目的影响是大大减少了互联网服务和Web服务器的功耗，这些服务器占美国电力消耗的8%。 此外，该项目的成果对环境产生了深远的影响，因为通过减少能源需求，发电机每年产生的二氧化碳量大大减少。

The objective of this project is to develop a theoretic framework and a hierarchical methodology for autonomic power and performance management of distributed computing centers through (a) online modeling, monitoring, and analysis of power consumption and performance; (b) adaptive learning and automatically identifying strategies to minimize power consumptions while maintaining the required quality of service requirements for a wide range of workloads and applications; and (c) dynamically reconfigure the computing, storage and network resources according to the selected optimization strategies. The proposed methodology exploits the emerging hardware and software standards to improve power efficiency of processors and devices such as such as Intels quickstart and SpeedStep processors, disk spindown, power aware page allocation and RDRAM that offer a wider range of low-power states and reducing the cost of transitions. The impact of this project is in the significant reduction of power consumptions of Internet services and Web servers that account for 8% of the US electricity consumption. In addition, the project results have a profound impact on the environment because by reducing the demand for energy, the amount of CO2 produced each year by electricity generators is reduced significantly.