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European Conference on Parallel Processing

Euro-Par 2011: Euro-Par 2011: Parallel Processing Workshops pp 408–415Cite as

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Use of HPC-Techniques for Large-Scale Data Migration

Use of HPC-Techniques for Large-Scale Data Migration

  • Jan Dünnweber30,
  • Valentin Mihaylov30,
  • René Glettler30,
  • Volker Maiborn30 &
  • …
  • Holger Wolff30 
  • Conference paper
  • 1425 Accesses

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 7155)

Abstract

Any re-design of a distributed legacy system requires a migration which involves numerous complex data replication and transformation steps. Migration procedures can become quite difficult and time-consuming, especially when the setup (i.e., the employed databases, encodings, formats etc.) of the legacy and the target system fundamentally differ, which is often the case with finance data, grown over decades. We report on experiences from a real-world project: the recent migration of a customer loyalty system from a COBOL-operated mainframe to a modern service-oriented architecture. In this context, we present our easy-to-adopt solution for running most replication steps in a high-performance manner: the QuickApply HPC-software which helps minimizing the replication time, and, thereby, the overall downtime of the migration. Business processes can be kept up and running most of the time, while pre-extracted data already pass a variety of platforms and representations toward the target system. We combine the advantages of traditional migration approaches: transformations, which require the interruption of business processes are performed with static data only, they can be made undone in case of a failure and terminate quickly, due to the use of parallel processing.

Keywords

  • Business Process
  • Legacy System
  • Target System
  • Work Unit
  • Loyalty Program

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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Author information

Authors and Affiliations

  1. MaibornWolff GmbH, München, Germany

    Jan Dünnweber, Valentin Mihaylov, René Glettler, Volker Maiborn & Holger Wolff

Authors
  1. Jan Dünnweber
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  2. Valentin Mihaylov
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  3. René Glettler
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  4. Volker Maiborn
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  5. Holger Wolff
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Editor information

Editors and Affiliations

  1. Scilytics, Koellnerhofgasse 3/15A, 1010, Vienna, Austria

    Michael Alexander

  2. ICAR-CNR, Via P. Castellino, 111, 80131, Napoli, Italy

    Pasqua D’Ambra

  3. University of Amsterdam, 1090, Amsterdam, Netherlands

    Adam Belloum

  4. Innovative Computing Laboratory, The University of Tennessee, USA

    George Bosilca

  5. Department of Experimental Medicine and Clinic, University Magna Græcia, 88100, Catanzaro, Italy

    Mario Cannataro

  6. Computer Science Department, University of Pisa, Italy

    Marco Danelutto

  7. Second University of Naples, Italy

    Beniamino Di Martino

  8. TU München, Boltzmannstr. 3, 85748, Garching, Germany

    Michael Gerndt

  9. Equipe Runtime, INRIA Bordeaux Sud-Ouest, 33405, Talence Cedex, France

    Emmanuel Jeannot & Raymond Namyst & 

  10. Equipe HIEPACS, INRIA Bordeaux Sud-Ouest, 33405, Talence Cedex, France

    Jean Roman

  11. Oak Ridge National Laboratory, Computer Science and Mathematics Division, 37831-6164, Oak Ridge, TN, USA

    Stephen L. Scott

  12. Department of Scientific Computing, University of Vienna, Nordbergstr. 15/3C, 1090, Vienna, Austrial

    Jesper Larsson Traff

  13. Computer Science and Mathematics Division, Oak Ridge National Laboratory, 37831, Oak Ridge, TN, USA

    Geoffroy Vallée

  14. Technische Universität München, Germany

    Josef Weidendorfer

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© 2012 Springer-Verlag Berlin Heidelberg

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Cite this paper

Dünnweber, J., Mihaylov, V., Glettler, R., Maiborn, V., Wolff, H. (2012). Use of HPC-Techniques for Large-Scale Data Migration. In: Alexander, M., et al. Euro-Par 2011: Parallel Processing Workshops. Euro-Par 2011. Lecture Notes in Computer Science, vol 7155. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29737-3_45

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  • DOI: https://doi.org/10.1007/978-3-642-29737-3_45

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29736-6

  • Online ISBN: 978-3-642-29737-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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