|
An
Energy-Efficient Scheduling Algorithm Using Dynamic Voltage Scaling for
Parallel Applications on Clusters
Xiaojun Ruan†,
Xiao Qin†*,
Ziliang
Zong†,
Kiranmai Bellam†,
and Mais Nijim‡
†Department
of Computer Science and Software Engineering
Auburn
University, Auburn, AL 36849
{xruan,
xqin, zzong, kbellam}@eng.auburn.edu
‡Department
of Computer Science
University
of Southern Mississippi, Hattiesburg, MS 39406
mnijim@usm.edu
In
the past decade cluster computing platforms have been widely applied to
support a variety of scientific and commercial applications, many of
which are parallel in nature. However, scheduling parallel applications
on large scale clusters is technically challenging due to significant
communication latencies and high energy consumption. As such, shortening
schedule length and conserving energy consumption are two major concerns
in designing economical
and environmentally friendly clusters. In this paper, we propose
an energy-efficient scheduling algorithm (TDVAS) using the dynamic
voltage scaling technique to provide significant energy savings for
clusters. The TDVAS algorithm aims at judiciously leveraging processor
idle times to lower processor voltages (i.e., the dynamic voltage
scaling
technique or DVS), thereby reducing energy consumption
experienced by parallel applications running on clusters. Reducing
processor voltages, however, can inevitably lead to increased execution
times of parallel task. The salient feature of the TDVAS algorithm is to
tackle this problem by exploiting tasks precedence constraints. Thus,
TDVAS applies the DVS technique to parallel tasks followed by idle
processor times to conserve energy consumption without increasing
schedule lengths of parallel applications. Experimental results clearly
show that the TDVAS algorithm is conducive to reducing energy
dissipation in large-scale clusters without adversely affecting system
performance.
This
paper appeared in the Proceedings of the 16th IEEE International Conference
on Computer Communications and Networks (ICCCN), Honolulu, Hawaii, Aug.
2007.
*
Corresponding author. http://www.eng.auburn.edu/~xqin
Acknowledgment:
The
work reported in this paper was supported by the US National Science
Foundation under Grant No. CCF-0702781, Auburn University under a
startup grant, New Mexico Institute of Mining and Technology under Grant
No. 103295, the Intel Corporation under Grant No. 2005-04-070, and the
Altera Corporation under an equipment grant.
|