By Topic

Macropower: A coarse-grain power profiling framework for energy-efficient cloud computing

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Ziming Zhang ; Dept. of Comput. Sci. & Eng., Univ. of North Texas, Denton, TX, USA ; Song Fu

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.

Published in:

Performance Computing and Communications Conference (IPCCC), 2011 IEEE 30th International

Date of Conference:

17-19 Nov. 2011