QUAREM: Maximising QoE Through Adaptive Resource Management in Mobile MPSoC Platforms

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.

Phone‐nomenon 2.0: A compact thermal model for smartphones

“…where g ij is the pseudo thermal conductance between s i and s j . Then, we artificially force Equation (11) to be equal to Equation (10) and have…”

“…The authors in Ref. [11] used adaptive resource management to maximise QoE of mobile MPSoCs based on the proposed energy demand prediction, plug‐in (charging) time estimation and adaptive dynamic voltage and frequency scaling. In summary, to achieve better performance and QoS/QoE of smartphones, the above previous works used the simplified thermal model with several resistors and capacitors or simplified equations.…”

“…These simplified linear equations are suitable for on-line thermal management as well. To deal with quality-of-experience (QoE) or quality-of-service (QoS) for mobile SoCs, there are several studies [10,11] seeking better QoS/QoE. With users' QoS requirement, the authors in Ref.…”

Thermal and Energy Efficiency Management of Mobile MPSoC: A Review

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Content- and Lighting-Aware Adaptive Brightness Scaling for Improved Mobile User Experience

“…Consequently, this makes battery life an increasing concern among mobile device users, with about ninety percent of mobile users suffering from low-battery anxiety-the fear of running out of mobile battery power [3]. This indicates that, in addition to the optimal display brightness that meets the surrounding ambient light of the user [4], battery life also plays a significant role in user's QoE [5]. Therefore, there is need for an effective management strategy that maximises QoE while taking battery life into account.…”