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

Abstract

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