TY - JOUR
T1 - QUAREM
T2 - Maximising QoE Through Adaptive Resource Management in Mobile MPSoC Platforms
AU - Isuwa, Samuel
AU - Dey, Somdip
AU - Ortega, Andre P.
AU - Singh, Amit Kumar
AU - Al-Hashimi, Bashir M.
AU - Merrett, Geoff V.
N1 - Funding Information:
This study was supported by Petroleum Technology Development Fund (PTDF) with grant number 1526/19. All data supporting this study are openly available from the University of Southampton repository at https://doi.org/10.5258/SOTON/D2153 .
Publisher Copyright:
© 2022 Association for Computing Machinery.
PY - 2022/9/5
Y1 - 2022/9/5
N2 - 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.
AB - 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.
KW - adaptive resource management
KW - Battery budgeting
KW - heterogeneous MPSoC
KW - maximising user experience
KW - QoE-aware resource management
KW - quality of experience
UR - http://www.scopus.com/inward/record.url?scp=85138713242&partnerID=8YFLogxK
U2 - 10.1145/3526116
DO - 10.1145/3526116
M3 - Article
AN - SCOPUS:85138713242
SN - 1539-9087
VL - 21
JO - ACM Transactions on Embedded Computing Systems
JF - ACM Transactions on Embedded Computing Systems
IS - 4
M1 - 38
ER -