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DARAW: a new write buffer to improve parallel I/O energy-efficiency
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Symposium on Applied Computing archive
Proceedings of the 2009 ACM symposium on Applied Computing table of contents
Honolulu, Hawaii
SESSION: Operating systems track table of contents
Pages 299-304  
Year of Publication: 2009
ISBN:978-1-60558-166-8
Authors
Xiaojun Ruan  Auburn University, Auburn, AL
Adam Manzanares  Auburn University, Auburn, AL
Kiranmai Bellam  Auburn University, Auburn, AL
Xiao Qin  Auburn University, Auburn, AL
Ziliang Zong  South Dakota School of Mines and Technology, Rapid City, SD
Sponsor
SIGAPP: ACM Special Interest Group on Applied Computing
Publisher
ACM  New York, NY, USA
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ABSTRACT

In the past decades, parallel I/O systems have been used widely to support scientific and commercial applications. New data centers today employ huge quantities of I/O systems, which consume a large amount of energy. Most large-scale I/O systems have an array of hard disks working in parallel to meet performance requirements. Traditional energy conservation techniques attempt to place disks into low-power states when possible. In this paper we propose a novel strategy, which aims to significantly conserve energy while reducing average I/O response times. This goal is achieved by making use of buffer disks in parallel I/O systems to accumulate small writes to form a log, which can be transferred to data disks in a batch way. We develop an algorithm - dynamic request allocation algorithm for writes or DARAW - to energy efficiently allocate and schedule write requests in a parallel I/O system. DARAW is able to improve parallel I/O energy efficiency by the virtue of leveraging buffer disks to serve a majority of incoming write requests, thereby keeping data disks in low-power state for longer period times. Buffered requests are then written to data disks at a predetermined time. Experimental results show that DARAW can significantly reduce energy dissipation in parallel I/O systems without adverse impacts on I/O performance.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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Collaborative Colleagues:
Xiaojun Ruan: colleagues
Adam Manzanares: colleagues
Kiranmai Bellam: colleagues
Xiao Qin: colleagues
Ziliang Zong: colleagues