Scheduling Scientific Workflows to Meet Soft Deadlines in the Absence of Failure Models

  • Kassian Plankensteiner
  • Radu Prodan
  • Thomas Fahringer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6271)

Abstract

Highly distributed systems such as Clouds and Grids are used to execute complex scientific workflow applications by researchers from various areas of science. While scientists rightfully expect efficient and reliable execution of their applications, current systems often cannot deliver the required Quality of Service. We propose a dynamic execution and scheduling heuristic able to schedule workflow applications with a high degree of fault tolerance, while taking into account soft deadlines. Experimental results show that our method meets soft deadlines in volatile highly distributed systems in the absence of historic failure trace data or complex failure models of the target system.

References

  1. 1.
    Blaha, P., Schwarz, K., Madsen, G., Kvasnicka, D., Luitz, J.: WIEN2k: An Augmented Plane Wave plus Local Orbitals Program for Calculating Crystal Properties, Institute of Physical and Theoretical Chemistry, TU Vienna (2001)Google Scholar
  2. 2.
    Brandic, I., Pllana, S., Benkner, S.: Specification, planning, and execution of qos-aware grid workflows within the amadeus environment. Concurrency Computat.: Pract. Exper. (January 2008)Google Scholar
  3. 3.
    Buyya, R., Murshed, M.: GridSim: a toolkit for the modeling and simulation of distributed resource management and scheduling for Grid computing. Concurrency and Computation-Practice & Experience 14(13-15), 1175–1220 (2002)CrossRefMATHGoogle Scholar
  4. 4.
    Cotton, W.R., Pielke Sr., R.A., Walko, R.L., Liston, G.E., Tremback, C.J., Jiang, H., McAnelly, R.L., Harrington, J.Y., Nicholls, M.E., Carrio, G.G., McFadden, J.P.: RAMS 2001: Current status and future directions. Meteorology and Atmospheric Physics 82(1), 5–29 (2003)CrossRefGoogle Scholar
  5. 5.
    Guo, L., McGough, A., Akram, A., Colling, D., Martyniak, J.: Qos for service based workflow on grid. In: Proceedings of UK e-Science 2007 All Hands Meeting (January 2007)Google Scholar
  6. 6.
    Iosup, A., Jan, M., Sonmez, O., Epema, D.: On the dynamic resource availability in grids. In: 2007 8th IEEE/ACM International Conference on Grid Computing, pp. 26–33 (2007)Google Scholar
  7. 7.
    Kandaswamy, G., Mandal, A., Reed, D.: Fault tolerance and recovery of scientific workflows on computational grids. In: 8th IEEE International Symposium on Cluster Computing and the Grid, CCGRID 2008, pp. 777–782 (2008)Google Scholar
  8. 8.
    Plachetka, T.: POVRAY – persistence of vision parallel raytracer. In: Computer Graphics International, pp. 123–129 (1998)Google Scholar
  9. 9.
    Plankensteiner, K., Prodan, R., Fahringer, T.: A new fault tolerance heuristic for scientific workflows in highly distributed environments based on resubmission impact. In: Fifth IEEE International Conference on e-Science, e-Science 2009, pp. 313–320 (December 2009)Google Scholar
  10. 10.
    Plankensteiner, K., Prodan, R., Fahringer, T., Kertesz, A., Kacsuk, P.: Fault-tolerant behavior in state-of-the-art grid worklow management systems. Tech. Rep. TR-0091, Institute on Grid Information, Resource and Worklow Monitoring Services, CoreGRID - Network of Excellence (October 2007)Google Scholar
  11. 11.
    Topcuoglu, H., Hariri, S., Wu, M.: Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Transactions on Parallel and Distributed Systems 13(3), 260–274 (2002)CrossRefGoogle Scholar
  12. 12.
    Volkert, J.: Austrian grid: Overview on the project with focus on parallel applications. In: International Symposium on Parallel and Distributed Computing (2006)Google Scholar
  13. 13.
    Wieczorek, M., Siddiqui, M., Villazon, A., Prodan, R., Fahringer, T.: Applying advance reservation to increase predictability of workflow execution on the grid. In: Second IEEE International Conference on e-Science and Grid Computing (2006)Google Scholar
  14. 14.
    Yu, J., Buyya, R., Tham, C.: Qos-based scheduling of workflow applications on service grids. In: Proceedings of the 1st IEEE International … (January 2005)Google Scholar
  15. 15.
    Zhang, Y., Mandal, A., Koelbel, C., Cooper, K.: Combined fault tolerance and scheduling techniques for workflow applications on computational grids. In: 9th IEEE/ACM International Symposium on Cluster Computing and the Grid, CCGRID 2009, pp. 244–251 (2009)Google Scholar
  16. 16.
    Zheng, T., Woodside, M.: Heuristic optimization of scheduling and allocation for distributed systems with soft deadlines. In: Computer Performance, pp. 169–181 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Kassian Plankensteiner
    • 1
  • Radu Prodan
    • 1
  • Thomas Fahringer
    • 1
  1. 1.Institute of Computer ScienceUniversity of InnsbruckInnsbruckAustria

Personalised recommendations