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Security-Driven Scheduling for Data-Intensive Applications on Grids
Tao
Xie† and Xiao Qin‡
†
Department of Computer Science
San Diego State University
San Diego, California 92182
xie@cs.sdsu.edu
‡Department
of Computer Science and Software Engineering
Auburn
University, Auburn, AL 36849
xqin@eng.auburn.edu
http://www.eng.auburn.edu/~xqin
Security-sensitive
applications that access and generate large data sets are emerging in
various areas including bioinformatics and high energy physics. Data
grids provide such data-intensive applications with a large virtual
storage framework with unlimited power. However, conventional scheduling
algorithms for data grids are unable to meet the security needs of
data-intensive applications. In this paper we address the problem of
scheduling data-intensive jobs on data grids subject to security
constraints. Using a security- and data-aware technique, a dynamic
scheduling strategy is proposed to improve quality of security for
data-intensive applications running on data grids. To incorporate
security into job scheduling, we introduce a new performance metric,
degree of security deficiency, to quantitatively measure quality of
security provided by a data grid. Results based on a real-world trace
confirm that the proposed scheduling strategy significantly improves
security and performance over four existing scheduling algorithms by up
to 810% and 1478%, respectively.
This
paper appeared in
Cluster
Computing: The Journal of Networks, Software Tools and Applications, Special Issue:
Evaluation and Optimization of High-Performance Computing and Networking Systems, Guest Editors:
G.-Y Min and M. Ould-Khaoua, vol. 10, no. 2, pp. 145-153, June 2007.
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.
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