We propose to investigate new functionalities to add at low cost in actual large scale schedulers and programming standards, for a better use of the resources according to various objectives and criteria. Clearly, the principle of using several priority queues in operational batch schedulers is not the best solution since it prioritizes arbitrarily some jobs (or resources) which in turn may delay other jobs.
We propose to revisit the principles of existing schedulers after studying the main factors impacted by job submissions.
Then, we will propose novel efficient algorithms for optimizing the schedule for unconventional objectives like energy consumption and to design provable approximation multi-objective optimization algorithms for some relevant combinations of objectives (performance, fairness, energy consumption, etc.).
An important characteristic of the project is its right balance between theoretical analysis and practical implementation.
The most promising ideas will lead to integration in reference systems such as SLURM and OAR as well as new features in programming standards implementations such as MPI or OpenMP.
We expect MOEBUS results to impact further use of very large scale parallel platforms.