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In this paper, we study grid jobs submission latency. The latency highly impacts performances on production grids, due to its high values and variations. It is particularly prejudicial for determining the status and expected duration of jobs and it makes outliers detection difficult. In previous work, a probabilistic model of the latency has been presented. It allows to estimate the best timeout value considering a given distribution of jobs latencies. This time-out value is then used in a job resubmission strategy. The purpose of this paper is to evaluate to what extent updating this model with relevant contextual parameters can help to refine the latency estimation. Experiments on the EGEE production grid show that the choice of the resource broker or the computing site has a statistically significant influence on the jobs latency. We exploit this contextual information to propose a reliable job submission strategy.