User-centric Resource Management for Embedded Multi-core Processors

Shamsa, Elham; Kanduri, Anil; TaheriNejad, Nima; Probstl, Alma; Chakraborty, Samarjit; Rahmani, Amir M.; Liljeberg, Pasi

doi:10.1109/vlsid49098.2020.00025

Cited by 6 publications

(6 citation statements)

References 15 publications

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“…These approaches do not consider individual user plug-in times thus, missing potential opportunity to maximise the QoE by either falling short of the battery life goal or over constraining the cpufreq governor. In addition, a recent study [7] and our analysis have shown that there is significant variation in users' usage pattern and mostly dependent on context (such as time and location) as against the fixed hours and SOC levels utilised in BUQS [33] and [51] respectively.…”

Section: Related Workmentioning

confidence: 65%

“…To address the user specific optimisation, [58], [51] and [33] presented a model using a weighted combination of QoS and/or energy to manage system resources for each individual user. [58] provided a customised mechanism to achieve trade-off between QoS and energy toward enhancing user experience.…”

Section: Related Workmentioning

confidence: 99%

“…In this approach, a novel definition of QoS based on responsiveness and display quality was first presented, then the QoE model was built (which is based on energy and QoS) and the trade-off is performed depending on the user's preference. Shamsa et al [51] leveraged on the same model while incorporating individual's battery state of charge (SOC) for the trade-off. Lee et al [33] exploited the non-trivial relationship between users' battery-life goal and QoE in addition to the battery status and QoS towards maximising user experience.…”

Section: Related Workmentioning

confidence: 99%

“…State-of-the-art academic research efforts have considered approaches to balance instantaneous power consumption and QoS in order to improve QoE [9,11,15,22,57,58]. However, little research has considered how to perform longer-term budgeting of resource [17,33,51,52] and, approaches that have, miss potential QoE maximisation because they are oblivious to the user's desired plug-in time (regarded as the time of the day when the user normally charges the device) and/or energy demand. This results in techniques that either fall short of users' battery life expectations (i.e., the number of hours a user uses their device before charging) or over-constrain the DVFS governor.…”

Section: Introductionmentioning

confidence: 99%

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

Isuwa

Dey

Ortega

et al. 2022

ACM Trans. Embed. Comput. Syst.

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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.

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Section: Related Workmentioning

confidence: 65%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Isuwa

Dey

Ortega

et al. 2022

ACM Trans. Embed. Comput. Syst.

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“…CSA has been applied to both software [44] and hardware [52]. Following applications have benefited from CSA concepts (some of them under other terms such as adaptivity, autonomy, and goal-oriented systems): mobile applications [53], object tracking with smart cameras [24], [54], artificial intelligence [55], cloud computing [56], networks [57], operating systems [58], web [59], adaptive and dynamic compilation environment [60], Multi-Processor System-on-Chip (MPSoC) resource management [61], [62], (cyber-physical) SoC [52], mobile robots [63], industrial systems [64], [65], health monitoring [22] as well as single and multi-user active music environments [66].…”

Section: Self-adaptivenessmentioning

confidence: 99%

RoSA: A Framework for Modeling Self-Awareness in Cyber-Physical Systems

et al. 2020

Self Cite

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The role of smart and autonomous systems is becoming vital in many areas of industry and society. Expectations from such systems continuously rise and become more ambitious: long lifetime, high reliability, high performance, energy efficiency, and adaptability, particularly in the presence of changing environments. Computational self-awareness promises a comprehensive assessment of the system state for sensible and well-informed actions and resource management. Computational self-awareness concepts can be used in many applications such as automated manufacturing plants, telecommunication systems, autonomous driving, traffic control, smart grids, and wearable health monitoring systems. Developing self-aware systems from scratch for each application is the most common practice currently, but this is highly redundant, inefficient, and uneconomic. Hence, we propose a framework that supports modeling and evaluation of various self-aware concepts in hierarchical agent systems, where agents are made up of self-aware functionalities. This paper presents the Research on Self-Awareness (RoSA) framework and its design principles. In addition, self-aware functionalities abstraction, data reliability, and confidence, which are currently provided by RoSA, are described. Potential use cases of RoSA are discussed. Capabilities of the proposed framework are showcased by case studies from the fields of healthcare and industrial monitoring. We believe that RoSA is capable of serving as a common framework for self-aware modeling and applications and thus helps researchers and engineers in exploring the vast design space of hierarchical agent-based systems with computational self-awareness.

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