Energy Saving Computational Models with Speed Scaling via Submodular Optimization

In this paper, we propose a new methodology for the speed scaling problem based on its link to scheduling with controllable processing times and submodular optimization. It results in faster algorithms for traditional speed scaling models, characterized by a common speed cost (or energy consumption) function. In addition, it handles efficiently the most general models with job-dependent speed cost functions, on a single machine and on multiple parallel machines, which to the best of our knowledge have not been addressed in prior research.