An Approach to Structure Simplifying for Large-Scale Workflows

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 113)

Abstract

In a large-scale workflow, the workflow structure needs to be simplified before execution so as to improve the completion performance. This paper puts forward an approach to structure simplifying for structured workflows. First, we present a task planning method based on differential evolution algorithm to map the tasks into available resources; then, based on the mapping relationship, the workflow structure will be simplified by task clustering. To evaluate the performance of the proposed approach, the proposed algorithms are evaluated through a comparison study using simulated workflows executed on a prototype workflow platform. The simulation results prove the effectiveness of our approach.

Keywords

Workflow simplifying Task planning Task clustering Differential evolution algorithm 

References

  1. 1.
    Tan W, Fan YS (2007) Dynamic workflow model fragmentation for distributed execution. Comput Ind 58(5):381–391CrossRefGoogle Scholar
  2. 2.
    Nanda MG, Chandra S, Sarkar V (2004) Decentralizing execution of composite web services. In: Proceedings of the 19th annual ACM SIGPLAN conference on object oriented programming, systems, languages, and applications, pp 170–187Google Scholar
  3. 3.
    Liu BX, Wang YF, Jia Y, Wu QY (2005) A role-based approach for decentralized dynamic service composition. J Softw 16(11):1859–1867CrossRefMATHGoogle Scholar
  4. 4.
    Bokhari SH (1988) Partitioning problems in parallel, pipelined, and distributed computing. IEEE Trans Comput (C-37):48–57Google Scholar
  5. 5.
    Andrea M, Stefano M (2005) Partitioning rules for orchestrating mobile information systems, personal and ubiquitous computing. Springer, London 9(5):291–300Google Scholar
  6. 6.
    Neyem A, Franco D, Ochoa SF, Pino JA (2007) Supporting mobile workflow with active entities. In: Proceedings of the 2007 11th international conference on computer supported cooperative work in design, pp 795–800Google Scholar
  7. 7.
    Choi Y, Zhao L (2005) Decomposition-based verification of cyclic workflow. Lect Notes Comput Sci. Springer, Berlin 3707:84–98Google Scholar
  8. 8.
    Li JQ, Fan YS (2002) Timing boundedness verification and analysis of workflow model. Comput Integr Manuf Syst 8(10):770–775Google Scholar
  9. 9.
    Deelman E (2010) Grids and clouds: making workflow applications work in heterogeneous distributed environments. Int J High Perform Comput Appl 24(3):284–298CrossRefGoogle Scholar
  10. 10.
    Singh G, Kesselman C, Deelman E (2006) Optimizing grid-based workflow execution. J Grid Comput 3:201–219Google Scholar
  11. 11.
    Storn R, Price K (1995) Differential evolution—a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical report TR-95-012, ICSIGoogle Scholar
  12. 12.
    Beaumont O, Boudet V, Robert Y (2002) The iso-level scheduling Heuristic for heterogeneous processors. In: Proceedings of the 10th euromicro workshop on parallel, distributed and network-based processing, pp 335–342Google Scholar
  13. 13.
    Tasgetiren MF, Pan QK, Liang YC, Suganthan PN (2007) A discrete differential evolution algorithm for the total earliness and tardiness penalties with a common due date on a single-machine. In: Proceedings of the IEEE symposium on computational intelligence in scheduling, pp 271–278Google Scholar
  14. 14.
    Wu XG, Zeng GZ (2010) Goals description and application in migrating workflow system. Expert Syst Appl 37(12):8027–8035CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyShanDong UniversityJinanChina
  2. 2.School of Mechanical, Electrical and Information EngineeringShandong University at WeihaiWeihaiChina

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