Section: New Results

Scheduling strategies for stochastic jobs

Following the observations of made in 7.7, we studied stochastic jobs (coming from neuroscience applications) which we want to schedule on a reservation-based platform (e.g. cloud, HPC).

The execution time of jobs is modeled using a (known) probability distribution. The platform to run the job is reservation-based, meaning that the user has to request fixed-length time slots for its job to be executed. The aim of this project is to study efficient strategies of reservation for an user given the cost associated to the machine. These reservations are all paid until a job is finally executed.

As a first step we derived efficient strategies without any additional asumptions [15]. This allowed us to set up properly the problem. These strategies were general enough that they could take as input any probability distributions, and performed better than any more natural strategies. Then we extended our strategies by including checkpoint/restart to well-chosen reservations in order to avoid wasting the benefits of work during underestimated reservations [35]. We were able to develop a fully polynomial-time approximation for continuous distribution of job execution time whose performance we then experimentally studied.

The final works of this project focused on the case without checkpointing: we studied experimentally how the strategies developed in [15] would perform in a parallel setup and showed that they improve both system utilization and job response time. Finally we started to study the robustness of such solutions when the job distributions were not perfectly known [19] and observed that the performance were still correct even with a very low quantity of information.