Scheduling and planning job execution of loosely coupled applications

  • Authors:

  • Enis Afgan;Purushotham Bangalore;Tibor Skala

  • Affiliations:

  • Department of Biology, Emory University, Atlanta, USA 30322;Department of Computer and Information Sciences, University of Alabama at Birmingham, Birmingham, USA 35294;University of Zagreb, Zagreb, Croatia 10 000

  • Venue:
  • The Journal of Supercomputing
  • Year:
  • 2012

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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%.