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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)

Abstract

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.

Keywords

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

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

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