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SAHA: A Scheduling Algorithm for Security-Sensitive Jobs on Data Grids

Tao Xie and Xiao Qin*

Department of Computer Science
New Mexico Institute of Mining and Technology
801 Leroy Place, Socorro, New Mexico 87801-4796
{xietao, xqin}@cs.nmt.edu

Security-sensitive applications that access and generate large data sets are emerging in various areas such as bioinformatics and high energy physics. Data grids provide data-intensive applications with a large virtual storage framework with unlimited power. However, conventional scheduling algorithms for data grids are inadequate to meet the security needs of data-intensive applications. To remedy this deficiency, we address in this paper the problem of scheduling data-intensive jobs on data grids subject to security constraints. Using a security- and data-aware technique, SAHA (Security-Aware and Heterogeneity-Aware scheduling strategy) is proposed to improve quality of security for data-intensive applications running on data grids. Results based on real-world traces show that the proposed scheduling scheme dramatically improves security and performance over two existing scheduling algorithms.

This paper appeared in Proc. IEEE/ACM 6th Int'l Symp. Cluster Computing and the Grid (CCGrid), 2nd Int'l Workshop on Cluster Security, May 2006.

* Contact author.  http://www.cs.nmt.edu/~xqin