[Back] [PDF]  An Adaptive Energy-Conserving Strategy for Parallel Disk Systems  Mais Nijim School of Computing University of Southern Mississippi Hattiesburg, MS 39406 mais.nijim@usm.edu http://orca.st.usm.edu/~mais   Adam Manzanares, Xiao Qin  Department of Computer Science and Software Engineering Auburn University, Auburn, AL 36849 {acm0008,xqin}@auburn.edu http://www.eng.auburn.edu/~xqin In the past decade parallel disk systems have been highly scalable and able to alleviate the problem of disk I/O bottleneck, thereby being widely used to support a wide range of data- intensive applications. Optimizing energy consumption in parallel disk systems has strong impacts on the cost of backup power-generation and cooling equipment, because a significant fraction of the operation cost of data centres is due to energy consumption and cooling. Although a variety of parallel disk systems were developed to achieve high performance and energy efficiency, most existing parallel disk systems lack an adaptive way to conserve energy in dynamically changing workload conditions. To solve this problem, we develop an adaptive energy-conserving algorithm, or DCAPS, for parallel disk systems using the dynamic voltage scaling technique that dynamically choose the most appropriate voltage supplies for parallel disks while guaranteeing specified performance (i.e., desired response times) for disk requests. We conduct extensive experiments to quantitatively evaluate the performance of the proposed energy-conserving strategy. Experimental results consistently show that DCAPS significantly reduces energy consumption of parallel disk systems in a dynamic environment over the same disk systems without using the DCAPS strategy. This paper appeared in the Proceedings of the 12th IEEE International Symposium on Distributed Simulation and Real Time Applications (DS-RT’08), Vancouver, British Columbia, Canada, Oct. 2008. 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, and No. DUE-0621307, and Auburn University under a startup grant.