Advertisement

Concepts and Algorithms of Mapping Grid-Based Workflow to Resources Within an SLA Context

  • Dang Minh Quan
  • Odej Kao
  • Jörn Altmann
Chapter
Part of the Computer Communications and Networks book series (CCN)

Abstract

With the popularity of Grid-based workflow, ensuring the Quality of Service (QoS) for workflow by Service Level Agreements (SLAs) is an emerging trend in the business grid. Among many system components for supporting SLA-aware Grid-based workflow, the SLA mapping mechanism is allotted an important position as it is responsible for assigning sub-jobs of the workflow to Grid resources in a way that meets the user’s deadline and minimizes costs. To meet those requirements, the resource in each Grid site must be reserved and the user must provide the estimated runtime of each sub-job correlated with a resource configuration. With many different kinds of sub-jobs and resources, the process of mapping a Grid-based workflow within an SLA context defines an unfamiliar and difficult problem. To solve this problem, this chapter describes related concepts and mapping algorithms. In particular, several suboptimization algorithms to map sub-jobs of the workflow to the Grid resources within an SLA context are described. The simulation results show the efficiency of those mapping algorithms.

Keywords

Feasible Solution Time Slot Data Transfer Critical Path Service Level Agreement 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Deelman, E., Blythe, J., Gil, Y., Kesselman, C., Mehta, G., Patil, S., Su, M., Vahi, K. and Livny, M. Pegasus: Mapping Scientific Workflows onto the Grid. In The 2nd European Across Grids Conference, Nicosia, Cyprus, LNCS Springer Press, (2004), pp. 11–20.Google Scholar
  2. 2.
    Georgakopoulos, D., Hornick, M., and Sheth, A. An Overview of workflow management: From Process Modeling to Workflow Automation Infrastructure, Distributed and Parallel Databases, 3, 2 (1995), 119–153.CrossRefGoogle Scholar
  3. 3.
    Fischer, L. Workflow Handbook 2004, Future Strategies Inc., Lighthouse Point, FL, USA.Google Scholar
  4. 4.
    Singh, M. P. and Vouk, M. A. Scientific Workflows: Scientific Computing Meets Transactional Workflows. 1997 (available at http://www.csc.ncsu.edu/faculty/mpsingh/papers/databases/workflows/sciworkflows.html, accessed on March, 9, 2008)
  5. 5.
    Ludtke, S., Baldwin, P. and Chiu, W. EMAN: Semiautomated Software for High-Resolution Single-Particle Reconstruction. Journal of Structure Biology, 128, (1999), 146–157.Google Scholar
  6. 6.
    Berriman, G. B., Good, J. C., Laity, A. C. (2003) Montage: a Grid Enabled Image Mosaic Service for the National Virtual Observatory. ADASS, 13, (2003), 145–1 167.Google Scholar
  7. 7.
    Lovas, R., Dzsa, G., Kacsuk, P., Podhorszki, N., Drtos, D. Workflow Support for Complex Grid Applications: Integrated and Portal Solutions. In The 2nd European Across Grids Conference, Nicosia, Cyprus, LNCS Springer Press, (2004), pp.129–138.Google Scholar
  8. 8.
    Spooner, D. P., Jarvis, S. A., Cao, J., Saini, S. and Nudd, G. R. Local Grid Scheduling Techniques Using Performance Prediction. IEEE Proceedings – Computers and Digital Techniques, 150, 2 (2003), pp. 87–96.CrossRefGoogle Scholar
  9. 9.
    Hovestadt, M. Scheduling in HPC Resource Management Systems: Queuing vs. Planning, In the 9th Workshop on JSSPP at GGF8, Washington, USA, LNCS Springer Press, (2003), pp. 1–20.Google Scholar
  10. 10.
    Wolski, R. Experiences with Predicting Resource Performance On-line in Computational Grid Settings. ACM SIGMETRICS Performance Evaluation Review, 30, 4 (2003), 41–49.CrossRefGoogle Scholar
  11. 11.
    McGough,S., Afzal, A., Darlington, J., Furmento, N., Mayer, A. and Young, L. Making the Grid Predictable through Reservations and Performance Modelling. The Computer Journal, 48, 3 (2005), 358–368.CrossRefGoogle Scholar
  12. 12.
    Zeng, L., Benatallah, B., Ngu, A., Dumas, M., Kalagnanam, J., Chang, H. QoS-Aware Middleware for Web Services Composition. IEEE Transactions on Software Engineering, 30, 2004.Google Scholar
  13. 13.
    Brandic, I., Benkner, S., Engelbrecht, G. and Schmidt, R. QoS Support for Time-Critical Grid Workflow Applications. In The first International Conference on e-Science and Grid Computing 2005, Melbourne, Australia, IEEE Computer Society Press, (2005), pp. 108–115.Google Scholar
  14. 14.
    Quan, D.M. A Framework for SLA-aware execution of Grid-based workflows, PhD thesis - University of Paderborn, Germany, 2006.Google Scholar
  15. 15.
    Quan, D.M., Altmann, J. Mapping of SLA-based workflows with light communication onto Grid resources. In The 4th International Conference on Grid Service Engineering and Management – GSEM 2007, Leipzig, Germany, LNI GI Press, (2007), pp. 135–145.Google Scholar

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.School of Information TechnologyInternational University in GermanyBruchsalGermany
  2. 2.Electrical Engineering and Computer ScienceTechnical University BerlinBerlinGermany

Personalised recommendations