Cluster Computing

, Volume 10, Issue 2, pp 145–153 | Cite as

Security-driven scheduling for data-intensive applications on grids



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.


Scheduling Security-sensitive application Data grid Degree of security deficiency 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Chervenak, A., Foster, I., Kesselman, C., Salisbury, C., Tuecke, S.: The data grid: towards an architecture for the distributed management and analysis of large scientific datasets. J. Netw. Comput. Appl. 23, 187–200 (2001) Google Scholar
  2. 2.
    Foster, I., Kesselman, C., Tuecke, S.: The anatomy of the grid: enabling scalable virtual organizations. Int. Journal Supercomput. Appl. 15(3), 200–222 (2001) Google Scholar
  3. 3.
    Keahey, K., Welch, V.: Fine-grain authorization for resource management in the grid environment. In: Proc. Int’l Workshop Grid Computing, 2002 Google Scholar
  4. 4.
    Novotny, J., Tuecke, S., Welch, V.: An online credential repository for the grid: MyProxy. In: Proc. Int’l Symp. High Performance Distributed Computing, August 2001 Google Scholar
  5. 5.
    Park, S.-M., Kim, J.-H.: Chameleon: a resource scheduler in a data grid environment. In: Proc. Int’l Symp. Cluster Computing and the Grid, 2003 Google Scholar
  6. 6.
    Qin, X., Jiang, H.: Data grids: supporting data-intensive applications in wide area networks. In: Yang, L., Guo, M.-Y. (eds.) High Performance Computing: Paradigm and Infrastructure, Wiley, Hoboken (2004) Google Scholar
  7. 7.
    Ranganathan, K., Foster, I.: Decoupling computation and data scheduling in distributed data-intensive applications. In: Proc. IEEE Int. Symp. High Performance Distributed Computing, 2002, pp. 352–358 Google Scholar
  8. 8.
    Song, S., Kwok, Y.-K., Hwang, K.: Trusted job scheduling in open computational grids: security-driven heuristics and a fast genetic algorithms. In: Proc. Int’l Symp. Parallel and Distributed Processing, 2005 Google Scholar
  9. 9.
    Welch, V., Siebenlist, F., Foster, I., Bresnahan, J., Czajkowski, K., Gawor, J., Kesselman, C., Meder, S., Pearlman, L., Tuecke, S.: Security for grid services. In: Proc. Int’l Symp. High Performance Distr. Computing, 2003 Google Scholar
  10. 10.
    Winton, L.: Data grids and high energy physics: a Melbourne perspective. Space Sci. Rev. 107(1–2), 523–540 (2003) CrossRefGoogle Scholar
  11. 11.
    Xie, T., Qin, X., Sung, A.: SAREC: a security-aware scheduling strategy for real-time applications on clusters. In: Proc. 34th Int’l Conf. Parallel Processing, Norway, June 2005 Google Scholar
  12. 12.
    Xie, T., Qin, X.: Enhancing security of real-time applications on grids through dynamic scheduling. In: Proc. 11th Workshop Job Scheduling Strategies for Parallel Processing, MA, June 2005 Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  1. 1.Department of Computer ScienceSan Diego State UniversitySan DiegoUSA
  2. 2.Department of Computer ScienceNew Mexico Institute of Mining and TechnologySocorroUSA

Personalised recommendations