Energy Efficient Computing through Productivity-Aware Frequency Scaling

Research output: Contribution to book/Conference proceedings/Anthology/ReportConference contributionContributedpeer-review

Contributors

  • Lesandro Ponciano - , Chair of Systems Engineering (Author)
  • Andrey Brito - (Author)
  • Lívia M. R. Sampaio - (Author)
  • Francisco Vilar Brasileiro - (Author)

Abstract

This paper proposes a new policy for dynamic frequency scaling: productivity-aware frequency scaling (PAFS). PAFS aims at optimizing energy consumptions while still satisfying performance requirements of a given application. In contrast to the commonly-used on demand frequency scaling, PAFS may keep the processor in a power save state even in high CPU-usage situations. This will be the case as long as the application (or set of applications) for which productivity is to be preserved presents acceptable performance (e.g., as stablished by a QoS contract). Our experiments show savings of up to 23.65% in energy consumption when compared to the commonly used on demand DFS policy with no performance degradation for the productivity metric. PAFS is, therefore, binded to a single or a set of applications running in a machine. Nevertheless, compared to previous approaches to application-specific frequency scaling, PAFS does not require modifying the application or a calibration process. PAFS requires only a productivity metric which may already be exported by an application (e.g., through a log file, such as response time or throughput in an Apache web server) or which may be computed through a simple program or script.

Details

Original languageEnglish
Title of host publication2012 Second International Conference on Cloud and Green Computing
PublisherIEEE, New York [u. a.]
Pages191-198
Number of pages8
ISBN (print)978-1-4673-3027-5
Publication statusPublished - 2012
Peer-reviewedYes

External IDs

Scopus 84874637033
ORCID /0000-0002-5724-0094/work/142241499

Keywords

Research priority areas of TU Dresden

Sustainable Development Goals