Experimental study demonstrates enhanced user experience and battery life on mobile devices, indicating the utility of adaptive workload clustering for power management.
As mobile devices become ubiquitous, the need for improved quality of experience (QoE) continues to place increasing demands on these devices. Delivering consistent and high QoE while managing various applications and user requirements on these energy-constrained devices remains a significant challenge. Dynamic voltage and frequency scaling (DVFS) and display brightness scaling are two commonly used power-saving techniques. We argue that existing approaches are not efficient in maximising user experience, especially in handling a variety of applications with varying requirements while meeting users’ daily battery life expectations. In this paper, we propose an adaptive approach that dynamically adjusts DVFS and display brightness scaling to maximise the user experience by considering the user’s expected battery life and a wide variety of mobile workload characteristics. Our method employs K-means clustering to classify diverse workloads at runtime and then assigns the appropriate DVFS and brightness settings based on the clusters and contexts of the workload and energy budget to achieve the expected battery life. Experimental evaluations conducted on real-world mobile devices demonstrate the effectiveness of our approach, with average improvements in QoE and battery life of up to 9% and 35%, respectively, compared to the state-of-the-art.
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Isuwa et al. (2026) studied this question.
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