A Provenance-Based Fault Tolerance Mechanism for Scientific Workflows

  • Daniel Crawl
  • Ilkay Altintas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5272)


Capturing provenance information in scientific workflows is not only useful for determining data-dependencies, but also for a wide range of queries including fault tolerance and usage statistics. As collaborative scientific workflow environments provide users with reusable shared workflows, collection and usage of provenance data in a generic way that could serve multiple data and computational models become vital. This paper presents a method for capturing data value- and control- dependencies for provenance information collection in the Kepler scientific workflow system. It also describes how the collected information based on these dependencies could be used for a fault tolerance framework in different models of computation.


Fault Tolerance Provenance Information Port Condition Fault Tolerance Mechanism Produce Output Data 
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.


  1. 1.
    Freire, J., Silva, C., Callahan, S., Santos, E., Scheidegger, C., Vo, H.: Managing Rapidly-Evolving Scientific Workflows. In: Proceedings of International Provenance and Annotation Workshop, pp. 10–18 (2006)Google Scholar
  2. 2.
    Ludäscher, B., Altintas, I., Berkley, C., Higgins, D., Jaeger-Frank, E., Jones, M., Lee, E., Tao, J., Zhao, Y.: Scientific Workflow Management and the Kepler System. Special Issue: Workflow in Grid Systems. Concurrency and Computation: Practice & Experience 18(10), 1039–1065 (2006)CrossRefGoogle Scholar
  3. 3.
    Jaeger-Frank, E., Crosby, C., Memon, A., Nandigam, V., Arrowsmith, J., Conner, J., Altintas, I., Baru, C.: A Three-Tier Architecture for LiDAR Interpolation and Analysis. In: Proceedings of International Workshop on Workflow Systems in e-Science, pp. 920–927 (2006)Google Scholar
  4. 4.
    Altintas, I., Barney, O., Jaeger-Frank, E.: Provenance Collection Support in the Kepler Scientific Workflow System. In: Proceedings of International Provenance and Annotation Workshop, pp. 118–132 (2006)Google Scholar
  5. 5.
    Altintas, I., et al.: Provenance in Kepler-based Scientific Workflow Systems. In: Microsoft e-Science Workshop, poster (2007)Google Scholar
  6. 6.
    Goderis, A., Brooks, C., Altintas, I., Lee, E.A., Goble, C.: Composing Different Models of Computation in Kepler and Ptolemy II. In: Proceedings of the International Conference on Computational Science (2007)Google Scholar
  7. 7.
    Myers, A.: JFlow: practical mostly-static information flow control. In: Proceedings Symposium on Principles of Programming Languages, pp. 228–241 (1999)Google Scholar
  8. 8.
    Haldar, V., Chandra, D., Franz, M.: Dynamic Taint Propagation for Java. In: Proceedings of Computer Security Applications Conference, pp. 303–311 (2005)Google Scholar
  9. 9.
    Wall, L., Christiansen, T., Orwant, J.: Programming Perl, 3rd edn. O’Reilly, SebastopolGoogle Scholar
  10. 10.
    Mitasova, H., Mitas, L., Harmon, R.: Simultaneous spline interpolation and topographic analysis for lidar elevation data: methods for open source GIS. IEEE GRSL 2(4), 375–379 (2005)Google Scholar
  11. 11.
    Miles, S., Groth, P., Branco, M., Moreau, L.: The Requirements of Recording and Using Provenance in e-Science Experiments. Journal of Grid Computing 5(1), 1–25 (2007)CrossRefGoogle Scholar
  12. 12.
    Zhao, Y., Wilde, M., Foster, I.: Applying the Virtual Data Provenance Model. In: Proceedings of International Provenance and Annotation Workshop, pp. 148–161 (2006)Google Scholar
  13. 13.
    Wootten, I., Rana, O., Rajbhandari, S.: Recording Actor State in Scientific Workflows. In: Proceedings of International Provenance and Annotation Workshop, pp. 109–117 (2006)Google Scholar
  14. 14.
    Ludäscher, B., Podhorszki, N., Altintas, I., Bowers, S., McPhillips, T.: From Computation Models to Models of Provenance: The RWS Approach. Concurrency and Computation: Practice & Experience 2(5), 507–518 (2007)Google Scholar
  15. 15.
    Plankensteiner, K., Prodan, R., Fahringer, T., Kertesz, A., Kacsuk, P.: Fault-tolerant behavior in state-of-the-art Grid Workflow Management Systems. TR-0091, CoreGRID (2007)Google Scholar
  16. 16.
    Fahringer, T., Prodan, R., Duan, R., Nerieri, F., Podlipnig, S., Qin, J., Siddiqui, M., Truong, H., Villazon, A., Wieczorek, M.: ASKALON: A Grid Application Development and Computing Environment. In: Proceedings of International Workshop on Grid Computing (2005)Google Scholar
  17. 17.
    Bowers, S., Ludäscher, B., Ngu, A., Critchlow, T.: Enabling Scientific Workflow Reuse through Structured Composition of Dataflow and Control-Flow. In: IEEE Workshop on Workflow and Data Flow for Scientific Applications (2006)Google Scholar
  18. 18.
    Feng, T.H., Lee, E.A.: Real-Time Distributed Discrete-Event Execution with Fault Tolerance. In: Proceedings of IEEE Real-Time and Embedded Technology and Applications Symposium (2008)Google Scholar
  19. 19.
    Laszewski, G., Hategan, M.: Workflow Concepts of the Java CoG Kit. Journal of Grid Computing 3(3-4), 239–258 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Daniel Crawl
    • 1
  • Ilkay Altintas
    • 1
  1. 1.San Diego Supercomputer Center, UCSDLa JollaUSA

Personalised recommendations