Title: Stochastic execution time modelling to improve scheduling performance in high performance computing
Current batch schedulers are based on the use of a user-provided execution time estimate. It is known that this estimate is almost always wrong. One direction of research is therefore to try to accurately predict the execution time of tasks, in particular via the analysis of their code. However, this approach has never succeeded. We assume that exact prediction of execution times is too complicated to be profitable, if not impossible. We will therefore consider execution times as random variables, use statistics to qualify them, and incorporate this knowledge into the batch scheduler. This presentation will present the first results obtained during a pre-thesis internship.