Maximizing Profit in Energy-Efficient Moldable Task Execution with Deadline

We consider static scheduling of parallelizable tasks onto machines with frequency scaling for the case that not all tasks can be executed prior to a deadline. We model this scenario from a HPC cluster operator’s perspective. We solve the combinatorial optimization problem to maximize the operator’s profit by integer linear programming and by a heuristic. We evaluate the heuristic with synthetic benchmark task sets and demonstrate that it achieves at most 20 % less profit than the solution via linear programming, so that it can be used for large task sets where the latter is not feasible anymore.