Security-driven scheduling for data-intensive applications on grids

Authors:
Tao Xie;Xiao Qin
Affiliations:
Department of Computer Science, San Diego State University, San Diego, USA 92182;Department of Computer Science, New Mexico Institute of Mining and Technology, Socorro, USA 87801
Venue:
Cluster Computing
Year:
2007

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Abstract

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.