[Back] [PDF]  An Adaptive Energy-Conserving Strategy for Parallel Disk Systems Xiaojun Ruan, Adam Manzanares, Kiranmai Bellam,  Xiao Qin† Department of Computer Science and Software Engineering Auburn University, Auburn, AL 36830, USA {xzr0001, acm0008, kzb0008}@eng.auburn.edu, xqin@auburn.edu  Ziliang Zong Department of Mathematics and Computer Science South Dakota School of Mines and Technology Rapid City, SD 57701 ziliang.zong@sdsmt.edu 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 pre-determined time. Experimental results show that DARAW can significantly reduce energy dissipation in parallel I/O systems without adverse impacts on I/O performance. This paper appeared in the Proceedings of Proc. the 24th Annual ACM Symposium on Applied Computing, March 2009. Acknowledgments The work reported in this paper was supported by the US National Science Foundation under Grants No. CCF-0742187, No. CNS-0757778, No. CNS-0831502, No. OCI-0753305, No. DUE-0621307, and No. DUE-0830831, and Auburn University under a startup grant.