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

University Of Arizona, Tucson AZ

Investigators

Abstract

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.