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Scheduling and planning job execution of loosely coupled applications

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Abstract

Growth in availability of data collection devices has allowed individual researchers to gain access to large quantities of data that needs to be analyzed. As a result, many labs and departments have acquired considerable compute resources. However, effective and efficient utilization of those resources remains a barrier for the individual researchers because the distributed computing environments are difficult to understand and control. We introduce a methodology and a tool that automatically manipulates and understands job submission parameters to realize a range of job execution alternatives across a distributed compute infrastructure. Generated alternatives are presented to a user at the time of job submission in the form of tradeoffs mapped onto two conflicting objectives, namely job cost and runtime. Such presentation of job execution alternatives allows a user to immediately and quantitatively observe viable options regarding their job execution, and thus allows the user to interact with the environment at a true service level. Generated job execution alternatives have been tested through simulation and on real-world resources and, in both cases, the average accuracy of the runtime of the generated and perceived job alternatives is within 5%.

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Authors and Affiliations

  1. Department of Biology, Emory University, Atlanta, GA, 30322, USA

    Enis Afgan

  2. Department of Computer and Information Sciences, University of Alabama at Birmingham, Birmingham, AL, 35294, USA

    Purushotham Bangalore

  3. University of Zagreb, Getaldiceva 2, 10 000, Zagreb, Croatia

    Tibor Skala

Authors
  1. Enis Afgan
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  2. Purushotham Bangalore
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  3. Tibor Skala
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Correspondence to Enis Afgan.

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Afgan, E., Bangalore, P. & Skala, T. Scheduling and planning job execution of loosely coupled applications. J Supercomput 59, 1431–1454 (2012). https://doi.org/10.1007/s11227-011-0555-y

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  • DOI: https://doi.org/10.1007/s11227-011-0555-y

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