Isuwa, Samuel, Dey, Somdip ORCID: https://orcid.org/0000-0001-6161-4637, Ortega, Andre P., Singh, Amit Kumar, Al-Hashimi, Bashir M. and Merrett, Geoff V. (2022) QUAREM: Maximising QoE Through Adaptive Resource Management in Mobile MPSoC Platforms. ACM Transactions on Embedded Computing Systems, 21 (4). pp. 1-29.

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Abstract

Heterogeneous multi-processor system-on-chip (MPSoC) smartphones are required to offer increasing performance and user quality-of-experience (QoE), despite comparatively slow advances in battery technology. Approaches to balance instantaneous power consumption, performance and QoE have been reported, but little research has considered how to perform longer-term budgeting of resources across a complete battery discharge cycle. Approaches that have considered this are oblivious to the daily variability in the user’s desired charging time-of-day (plug-in time), resulting in a failure to meet the user’s battery life expectations, or else an unnecessarily over-constrained QoE. This paper proposes QUAREM, an adaptive resource management approach in mobile MPSoC platforms that maximises QoE while meeting battery life expectations. The proposed approach utilises a model that learns and then predicts the dynamics of the energy usage pattern and plug-in times. Unlike state-of-the-art approaches, we maximise the QoE through the adaptive balancing of the battery life and the quality of service (QoS) for the duration of the battery discharge. Our model achieves a good degree of accuracy with a mean absolute percentage error of 3.47% and 2.48% for the energy demand and plug-in times, respectively. Experimental evaluation on an off-the-shelf commercial smartphone shows that QUAREM achieves the expected battery life of the user within 20–25% energy demand variation with little or no QoE degradation.

Item Type:	Article
Status:	Published
DOI:	https://doi.org/10.1145/3526116
School/Department:	School of Science, Technology and Health
URI:	https://ray.yorksj.ac.uk/id/eprint/8585

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