Taming power peaks in mapreduce clusters

Authors:
Nan Zhu;Lei Rao;Xue Liu;Jie Liu;Haibin Guan
Affiliations:
Shanghai Jiaotong University, Shanghai, China;McGill University, Montreal, Canada;McGill University, Montreal, Canada;Microsoft Research, Redmond, USA;Shanghai Jiaotong University, Shanghai, China
Venue:
Proceedings of the ACM SIGCOMM 2011 conference
Year:
2011

Citing 1
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Proceedings of the 34th annual international symposium on Computer architecture

Handling more data with less cost: taming power peaks in MapReduce clusters

Proceedings of the Asia-Pacific Workshop on Systems
Handling more data with less cost: taming power peaks in mapreduce clusters

APSys'12 Proceedings of the Third ACM SIGOPS Asia-Pacific conference on Systems

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Abstract

Along with the surging service demands on the cloud, the energy cost of Internet Data Centers (IDCs) is dramatically increasing. Energy management for IDCs is becoming ever more important. A large portion of applications running on data centers are data-intensive applications. MapReduce (and Hadoop) has been one of the mostly deployed frameworks for data-intensive applications. Both academia and industry have been greatly concerned with the problem of how to reduce the energy consumption of IDCs. However the critical power peak problem for MapReduce clusters has been overlooked, which is a new challenge brought by the usage of MapReduce. We elaborate the power peak problem and investigate the cause of the problem in details. Then we design an adaptive approach to regulate power peaks.