Probabilistic and Dynamic Optimization of Job Partitioning on a Grid Infrastructure

  • Authors:
  • Tristan Glatard;Johan Montagnat;Xavier Pennec

  • Affiliations:
  • INRIA Sophia-Antipolis, France;INRIA Sophia-Antipolis, France;INRIA Sophia-Antipolis, France

  • Venue:
  • PDP '06 Proceedings of the 14th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing
  • Year:
  • 2006

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Abstract

Production grids have a potential for parallel execution of a very large number of tasks but also introduce a high overhead that significantly impacts the execution of short tasks. In this work, we present a strategy to optimize the partitioning of jobs on a grid infrastructure. This method takes into account the variability and the difficulty to model a multi-user large-scale environment used for production. It is based on probabilistic estimations of the grid overhead. We first study analytically modeled environments and then we show results on a real grid infrastructure. We demonstrate that this method leads to a significant time speed-up and to a substantial saving of the number of submitted tasks with respect to a blind maximal partitioning strategy.