Dynamic voltage frequency scaling for multi-tasking systems using online learning

This paper presents an extremely lightweight dynamic voltage and frequency scaling technique targeted towards modern multi-tasking systems. The technique utilizes processors runtime statistics and an online learning algorithm to estimate the best suited voltage and frequency setting at any given point in time. We implemented the proposed technique in Linux 2.6.9 running on an Intel PXA27x platform and performed experiments in both single and multi-task environments. Our measurements show that we can achieve the maximum energy savings of 49% and reduce the implementation overhead by a factor of 2 when compared to state of the art techniques.