Learning-Based Power Management for Multi-Core Processors via Idle Period Manipulation

Learning-based dynamic power management (DPM) techniques, being able to adapt to varying system conditions and workloads, have attracted lots of research attention recently. To the best of our knowledge, however, none of the existing learning-based DPM solutions are dedicated to power reduction in multi-core processors, although they can be utilized by treating each processor core as a standalone entity and conducting DPM for them separately. In this work, by including task allocation into our learning-based DPM framework for multi-core processors, we are able to manipulate idle periods on processor cores to achieve a better tradeoff between power consumption and system performance. Experimental results show that the proposed solution significantly outperforms existing DPM techniques.