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@PHDTHESIS{Holl:153150,
      author       = {Holl, Sonja},
      title        = {{A}utomated {O}ptimization {M}ethods for {S}cientific
                      {W}orkflows in e-{S}cience {I}nfrastructures},
      volume       = {24},
      school       = {Universität Bonn},
      type         = {Dr.},
      address      = {Jülich},
      publisher    = {Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag},
      reportid     = {FZJ-2014-02813},
      isbn         = {978-3-89336-949-2},
      series       = {Schriften des Forschungszentrums Jülich. IAS Series},
      pages        = {xvi, 182 S.},
      year         = {2014},
      note         = {Universität Bonn, Diss., 2014},
      abstract     = {Scientific workflows have emerged as a key technology that
                      assists scientists with the design, management, execution,
                      sharing and reuse of in silico experiments. Workflow
                      management systems simplify the management of scientific
                      workflows by providing graphical interfaces for their
                      development, monitoring and analysis. Nowadays, e-Science
                      combines such workflow management systems with large-scale
                      data and computing resources into complex research
                      infrastructures. For instance, e-Science allows the
                      conveyance of best practice research in collaborations by
                      providing workflow repositories, which facilitate the
                      sharing and reuse of scientific workflows. However,
                      scientists are still faced with different limitations while
                      reusing workflows. One of the most common challenges they
                      meet is the need to select appropriate applications and
                      their individual execution parameters. If scientists do not
                      want to rely on default or experience-based parameters, the
                      best-effort option is to test different workflow set-ups
                      using either trial and error approaches or parameter sweeps.
                      Both methods may be inefficient or time consuming
                      respectively, especially when tuning a large number of
                      parameters. Therefore, scientists require an effective and
                      efficient mechanism that automatically tests different
                      workflow set-ups in an intelligent way and will help them to
                      improve their scientific results. This thesis addresses the
                      limitation described above by defining and implementing an
                      approach for the optimization of scientific workflows. In
                      the course of this work, scientists’ needs are
                      investigated and requirements are formulated resulting in an
                      appropriate optimization concept. In a following step, this
                      concept is prototypically implemented by extending a
                      workflow management system with an optimization framework,
                      including general mechanisms required to conduct workflow
                      optimization. As optimization is an ongoing research topic,
                      different algorithms are provided by pluggable extensions
                      (plugins) that can be loosely coupled with the framework,
                      resulting in a generic and quickly extendable system. In
                      this thesis, an exemplary plugin is introduced which applies
                      a Genetic Algorithm for parameter optimization. In order to
                      accelerate and therefore make workflow optimization feasible
                      at all, e-Science infrastructures are utilized for the
                      parallel execution of scientific workflows. This is
                      empowered by additional extensions enabling the execution of
                      applications and workflows on distributed computing
                      resources. The actual implementation and therewith the
                      general approach of workflow optimization is experimentally
                      verified by four use cases in the life science domain. All
                      workflows were significantly improved, which demonstrates
                      the advantage of the proposed workflow optimization.
                      Finally, a new collaboration-based approach is introduced
                      that harnesses optimization provenance to make optimization
                      faster and more robust in the future.},
      keywords     = {Dissertation (GND)},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {412 - Grid Technologies and Infrastructures (POF2-412)},
      pid          = {G:(DE-HGF)POF2-412},
      typ          = {PUB:(DE-HGF)11},
      urn          = {urn:nbn:de:0001-2014022000},
      url          = {https://juser.fz-juelich.de/record/153150},
}