Redefining QoS and customizing the power management policy to satisfy individual mobile users

Yan, Kaige; Zhang, Xingyao; Tan, Jie; Fu, Xin

doi:10.1109/micro.2016.7783756

Cited by 28 publications

(13 citation statements)

References 19 publications

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“…We define a utility function ( ) to represent the application performance as user QoE at time . This utility function can be interpreted differently by users and by applications [51]. Although there are no golden rules for defining user QoE, many existing studies define the QoE as guaranteeing application performance beyond a certain level (e.g., required frame rates for video/gaming applications) [9,13,38].…”

Section: Problem Formulationmentioning

confidence: 99%

zTT

Kim

Bin

et al. 2021

Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services

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DVFS (dynamic voltage and frequency scaling) is a system-level technique that adjusts voltage and frequency levels of CPU/GPU at runtime to balance energy efficiency and high performance. DVFS has been studied for many years, but it is considered still challenging to realize a DVFS that performs ideally for mobile devices for two main reasons: i) an optimal power budget distribution between CPU and GPU in a power-constrained platform can only be defined by the application performance, but conventional DVFS implementations are mostly application-agnostic; ii) mobile platforms experience dynamic thermal environments for many reasons such as mobility and holding methods, but conventional implementations are not adaptive enough to such environmental changes. In this work, we propose a deep reinforcement learning-based frequency scaling technique, zTT. zTT learns thermal environmental characteristics and jointly scales CPU and GPU frequencies to maximize the application performance in an energy-efficient manner while achieving zero thermal throttling. Our evaluations for zTT implemented on Google Pixel 3a and NVIDIA JETSON TX2 platform with various applications show that zTT can adapt quickly to changing thermal environments, consistently resulting in high application performance with energy efficiency. In a high-temperature environment where a rendering application with the default mobile DVFS fails to keep producing more than a target frame rate, zTT successfully manages to do so even with 23.9% less average power consumption. CCS CONCEPTS• Human-centered computing → Ubiquitous and mobile computing systems and tools; • Software and its engineering → Power management.

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Section: Problem Formulationmentioning

confidence: 99%

zTT

Kim

Bin

et al. 2021

Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services

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“…Although these techniques can be a natural choice for simple architectural tuning, they provide no mechanism for a feedback thus being rigid to the addition of learning mechanism. Other approaches might use model-based heuristics [4,13,16,36,85,88] where decisions are guided using a system model that defines the relation between inputs and possible outputs. These methods lack guarantees or formal methodology making the runtime resource manager prone to errors in particular when encountering corner cases that might lead to system instability.…”

Section: Related Workmentioning

confidence: 99%

Hessle-Free

Moazzemi

Maity

Rahmani

et al. 2019

ACM Trans. Embed. Comput. Syst.

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As computing platforms increasingly embrace heterogeneity, runtime resource managers need to efficiently, dynamically, and robustly manage shared resources (e.g., cores, power budgets, memory bandwidth). To address the complexities in heterogeneous systems, state-of-the-art techniques that use heuristics or machine learning have been proposed. On the other hand, conventional control theory can be used for formal guarantees, but may face unmanageable complexity for modeling system dynamics of complex heterogeneous systems. We address this challenge through HESSLE-FREE (Heterogeneous Systems Leveraging Fuzzy Control for Runtime Resource Management): an approach leveraging fuzzy control theory that combines the strengths of classical control theory together with heuristics to form a light-weight, agile, and efficient runtime resource manager for heterogeneous systems. We demonstrate the efficacy of HESSLE-FREE executing on a NVIDIA Jetson TX2 platform (containing a heterogeneous multi-processor with a GPU) to show that HESSLE-FREE: 1) provides opportunity for optimization in the controller and stability analysis to enhance the confidence in the reliability of the system; 2) coordinates heterogeneous compute units to achieve desired objectives (e.g., QoS, optimal power references, FPS) efficiently and with lower complexity , and 3) eases the burden of system specification.

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“…However, smartphone usage can be used to infer the users' personality and even stress level [21,40]. K. Yan et al have re-defined the QoS definition for mobile devices and explored the optimal management policy based on users' personalities [60].…”

Section: Related Workmentioning

confidence: 99%

Toward Customized Hybrid Fuel-Cell and Battery-powered Mobile Device for Individual Users

Yan

Tan

Liu

et al. 2019

ACM Trans. Embed. Comput. Syst.

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Rapidly evolving technologies and applications of mobile devices inevitably increase the power demands on the battery. However, the development of batteries can hardly keep pace with the fast-growing demands, leading to short battery life, which becomes the top complaints from customers. In this article, we investigate a novel energy supply technology, fuel cell (FC), and leverage its advantages of providing long-term energy storage to build a hybrid FC-battery power system. Therefore, mobile device operation time is dramatically extended, and users are no longer bothered by battery recharging. We examine real-world smartphone usage data and find that a naive hybrid power system cannot meet many users’ highly diversified power demands. We thus propose an OS-level power management policy that reduces the device power consumption for each power peak to solve this mismatch. This technique trades the quality-of-service (QoS) for a larger FC ratio in the system and thus much longer device operation time. We further observe that the user’s personality largely determines his/her satisfaction with the QoS degradation and the operation time extension. Thus, applying a hybrid system with fixed configuration (i.e., peak throttling level coupled with corresponding FC/battery ratio) fails to satisfy every user. We then explore customized hybrid system configuration based on each individual user’s personality to deliver the optimal satisfaction for him/her. The experimental results show that our personality-aware hybrid FC-battery solution can achieve 4× longer operation time and 25% higher satisfaction score compared to the common setting for state-of-the-art mobile devices.

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Redefining QoS and customizing the power management policy to satisfy individual mobile users

Cited by 28 publications

References 19 publications

zTT

zTT

Hessle-Free

Toward Customized Hybrid Fuel-Cell and Battery-powered Mobile Device for Individual Users

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