IEEE International Performance Computing and Communications Conference (2011)
Orlando, FL, USA
Nov. 17, 2011 to Nov. 19, 2011
Song Fu , Department of Computer Science and Engineering, University of North Texas
Ziming Zhang , Department of Computer Science and Engineering, University of North Texas
Power and energy consumption has become a major concern in modern data centers and cloud systems. In order to develop efficient power management mechanisms for green clouds, we need a deep understanding of the influence of system configurations on the power consumption in real cloud systems. Power profiling provides such a vehicle. Existing fine-grain profiling approaches require special hardwired connections to the pins of individual hardware devices, which is not practical for large-scale production clouds. Moreover, they cannot provide a macroscopic view of the cloud-wide power dynamics. In this paper, we present macropower, a coarse-grain power and energy profiling framework. It provides a combination of hardware and software tools that achieves power/energy profiling at server granularity. It uses direct or derived measurements to isolate and combine influences from system components in cloud power profiles. It also generates the correlations between system activities and server/cloud-wide power/energy usage. We implement a prototype of macropower and test it in a cloud testbed. The profiled data are analyzed and the impact of system configurations on the server/cloud power usage is quantified, which is valuable for autonomic and energy-efficient management of cloud resources.
Song Fu, Ziming Zhang, "Macropower: A coarse-grain power profiling framework for energy-efficient cloud computing", IEEE International Performance Computing and Communications Conference, vol. 00, no. , pp. 1-8, 2011, doi:10.1109/PCCC.2011.6108061