  Heterogeneous processors, formed by binary compatible CPU cores with
different microarchitectures, enable energy reductions by better matching
processing capabilities and software application requirements. This new
hardware platform requires novel techniques to manage power and energy to fully
utilize its capabilities, particularly regarding the mapping of workloads to
appropriate cores. In this paper we validate relevant published work related to
power modelling for heterogeneous systems and propose a new approach for
developing run-time power models that uses a hybrid set of physical predictors,
performance events and CPU state information. We demonstrate the accuracy of
this approach compared with the state-of-the-art and its applicability to
energy aware scheduling. Our results are obtained on a commercially available
platform built around the Samsung Exynos 5 Octa SoC, which features the ARM
big.LITTLE heterogeneous architecture.
