Adaptive DVS management through elastic scheduling

Dynamic voltage scaling (DVS) is a technique used in modern microprocessors operated by battery to set voltage and frequency levels at proper values that meet performance requirements while minimizing energy con-sumption. Most of the present work on DVS manage-ment, however, is based on simplistic assumptions about the hardware characteristics that limit the real applica-bility of the proposed algorithms. Typical simplifying assumptions consider continuous processor speed, negli-gible overhead during voltage switching, task execution time linear with frequency, and tasks with equal power consumption. In this work, we enhance the task model to consider some of the real CPU characteristics, and inte-grate energy-aware algorithms with elastic scheduling to improve control performance of embedded systems run-ning on architectures offering a limited number of oper-ating modes. Implementation issues and experimental re-sults for the proposed algorithm are also discussed.