@article{KrottGerhardsSkorupaetal.2011,
  author    = {Krott, Daniel and Gerhards, Michael and Skorupa, Sascha and Sander, Volker},
  title     = {NHiLA - Bridging the Gap Between .NET and UNICORE},
  series = {UNICORE Summit 2011 : proceedings, 7-8 July 2011, Torun, Poland / Mathilde Romberg ... (Eds.)},
  journal   = {UNICORE Summit 2011 : proceedings, 7-8 July 2011, Torun, Poland / Mathilde Romberg ... (Eds.)},
  publisher = {Forschungszentrum J{\"u}lich},
  address   = {J{\"u}lich},
  isbn      = {9783893367504},
  pages     = {77 -- 86},
  year      = {2011},
  language  = {en}
}
@inproceedings{GerhardsSanderBelloum2012,
  author    = {Gerhards, Michael and Sander, Volker and Belloum, Adam},
  title     = {About the flexible Migration of Workflow Tasks to Clouds : Combining on- and off-premise Executions of Applications},
  series = {CLOUD COMPUTING 2012 : The Third International Conference on Cloud Computing, GRIDs, and Virtualization ; July 22-27, 2012 - Nice, France},
  booktitle = {CLOUD COMPUTING 2012 : The Third International Conference on Cloud Computing, GRIDs, and Virtualization ; July 22-27, 2012 - Nice, France},
  editor    = {Zimmermann, Wolf and Lee, Yong Woo and Demchenko, Yuri},
  publisher = {IARIA Journals},
  isbn      = {978-1-61208-216-5},
  pages     = {82 -- 87},
  year      = {2012},
  abstract  = {An increasing number of applications target their executions on specific hardware like general purpose Graphics Processing Units. Some Cloud Computing providers offer this specific hardware so that organizations can rent such resources. However, outsourcing the whole application to the Cloud causes avoidable costs if only some parts of the application benefit from the specific expensive hardware. A partial execution of applications in the Cloud is a tradeoff between costs and efficiency. This paper addresses the demand for a consistent framework that allows for a mixture of on- and off-premise calculations by migrating only specific parts to a Cloud. It uses the concept of workflows to present how individual workflow tasks can be migrated to the Cloud whereas the remaining tasks are executed on-premise.},
  language  = {en}
}
@inproceedings{GerhardsSanderBelloum2013,
  author    = {Gerhards, Michael and Sander, Volker and Belloum, Adam},
  title     = {Seamlessly enabling the use of cloud resources in workflows},
  series = {Cloud Computing 2013 : The Fourth International Conference on Cloud Computing, GRIDs, and Virtualization : May 27 - June 1, 2013 - Valencia, Spain},
  booktitle = {Cloud Computing 2013 : The Fourth International Conference on Cloud Computing, GRIDs, and Virtualization : May 27 - June 1, 2013 - Valencia, Spain},
  publisher = {Curren},
  address   = {Red Hook, NY},
  organization    = {International Conference on Cloud Computing, GRIDs, and Virtualization <4, 2013, Valencia>},
  isbn      = {978-1-61208-271-4},
  pages     = {108 -- 114},
  year      = {2013},
  language  = {en}
}
@article{GerhardsSanderZivkovicetal.2020,
  author    = {Gerhards, Michael and Sander, Volker and Zivkovic, Miroslav and Belloum, Adam and Bubak, Marian},
  title     = {New approach to allocation planning of many-task workflows on clouds},
  series = {Concurrency and Computation: Practice and Experience},
  volume    = {32},
  journal   = {Concurrency and Computation: Practice and Experience},
  number    = {2 Article e5404},
  publisher = {Wiley},
  address   = {Chichester},
  issn      = {1532-0634},
  doi       = {10.1002/cpe.5404},
  pages     = {1 -- 16},
  year      = {2020},
  abstract  = {Experience has shown that a priori created static resource allocation plans are vulnerable to runtime deviations and hence often become uneconomic or highly exceed a predefined soft deadline. The assumption of constant task execution times during allocation planning is even more unlikely in a cloud environment where virtualized resources vary in performance. Revising the initially created resource allocation plan at runtime allows the scheduler to react on deviations between planning and execution. Such an adaptive rescheduling of a many-task application workflow is only feasible, when the planning time can be handled efficiently at runtime. In this paper, we present the static low-complexity resource allocation planning algorithm (LCP) applicable to efficiently schedule many-task scientific application workflows on cloud resources of different capabilities. The benefits of the presented algorithm are benchmarked against alternative approaches. The benchmark results show that LCP is not only able to compete against higher complexity algorithms in terms of planned costs and planned makespan but also outperforms them significantly by magnitudes of 2 to 160 in terms of required planning time. Hence, LCP is superior in terms of practical usability where low planning time is essential such as in our targeted online rescheduling scenario.},
  language  = {en}
}