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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                               
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.