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 firstname.lastname@example.org ‡Department of Computer Science and Software Engineering
Auburn University, Auburn, AL 36849
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.