Energy considerations are important for Internet datacenters operators, and MapReduce is a common Internet datacenter application. In this work, we use the energy efficiency of MapReduce as a new perspective for increasing Internet datacenter productivity. We offer a framework to analyze software energy efficiency in general, and MapReduce energy efficiency in particular. We characterize the performance of the Hadoop implementation of MapReduce under different workloads. We also introduce quantitative models to guide operators and developers in improving the performance of MapReduce/Hadoop. A major, but somewhat unsurprising finding is that for workloads where the work rate is proportional to the amount of resources used, improving the performance as measured by traditional metrics such as job duration is equivalent to improving the performance as measured by lower energy consumed.
Title
Towards Energy Efficient MapReduce
Published
2009-08-05
Full Collection Name
Electrical Engineering & Computer Sciences Technical Reports
Other Identifiers
EECS-2009-109
Type
Text
Extent
23 p
Archive
The Engineering Library
Usage Statement
Researchers may make free and open use of the UC Berkeley Library’s digitized public domain materials. However, some materials in our online collections may be protected by U.S. copyright law (Title 17, U.S.C.). Use or reproduction of materials protected by copyright beyond that allowed by fair use (Title 17, U.S.C. § 107) requires permission from the copyright owners. The use or reproduction of some materials may also be restricted by terms of University of California gift or purchase agreements, privacy and publicity rights, or trademark law. Responsibility for determining rights status and permissibility of any use or reproduction rests exclusively with the researcher. To learn more or make inquiries, please see our permissions policies (https://www.lib.berkeley.edu/about/permissions-policies).
