A1 Refereed original research article in a scientific journal
UBAR: User- and Battery-aware Resource Management for Smartphones
Authors: Shamsa Elham, Pröbstl Alma, TaheriNejad Nima, Kanduri Anil, Chakraborty Samarjit, Rahmani Amir M., Liljeberg Pasi
Publisher: ASSOC COMPUTING MACHINERY
Publication year: 2021
Journal: ACM Transactions in Embedded Computing Systems
Journal acronym: ACM T EMBED COMPUT S
Article number: ARTN 23
Volume: 20
Issue: 3
Number of pages: 25
ISSN: 1539-9087
eISSN: 1558-3465
DOI: https://doi.org/10.1145/3441644
Abstract
Smartphone users require high Battery Cycle Life (BCL) and high Quality of Experience (QoE) during their usage. These two objectives can be conflicting based on the user preference at run-time. Finding the best trade-off between QoE and BCL requires an intelligent resource management approach that considers and learns user preference at run-time. Current approaches focus on one of these two objectives and neglect the other, limiting their efficiency in meeting users' needs. In this article, we present UBAR, User- and Battery-aware Resource management, which considers dynamic workload, user preference, and user plug-in/out pattern at run-time to provide a suitable trade-off between BCL and QoE. UBAR personalizes this trade-off by learning the user's habits and using that to satisfy QoE, while considering battery temperature and State of Charge (SOC) pattern to maximize BCL. The evaluation results show that UBAR achieves 10% to 40% improvement compared to the existing state-of-the-art approaches.
Smartphone users require high Battery Cycle Life (BCL) and high Quality of Experience (QoE) during their usage. These two objectives can be conflicting based on the user preference at run-time. Finding the best trade-off between QoE and BCL requires an intelligent resource management approach that considers and learns user preference at run-time. Current approaches focus on one of these two objectives and neglect the other, limiting their efficiency in meeting users' needs. In this article, we present UBAR, User- and Battery-aware Resource management, which considers dynamic workload, user preference, and user plug-in/out pattern at run-time to provide a suitable trade-off between BCL and QoE. UBAR personalizes this trade-off by learning the user's habits and using that to satisfy QoE, while considering battery temperature and State of Charge (SOC) pattern to maximize BCL. The evaluation results show that UBAR achieves 10% to 40% improvement compared to the existing state-of-the-art approaches.
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