Probabilistic and Dynamic Optimization of Job Partitioning on a Grid Infrastructure

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. Keywords : Grid Computing – Models and Tools – Heterogeneous Systems – Parallel Systems – Distributed Systems

• Type: Conference
• Year: 2006
• Where: PDP
• Authors: Tristan Glatard, Johan Montagnat, Xavier Pennec
• Keywords: Distributed And Parallel Computing, Grid, Grid Infrastructure, PDP 2006, Real Grid Infrastructure