User-Centric Scheduling and Governing on Mobile Devices with big.LITTLE Processors

Hsiu, Pi-Cheng; Tseng, Po-Hsien; Chen, Wei-Ming; Pan, Chun‐Jern; Kuo, Tei-Wei

doi:10.1145/2829946

Cited by 19 publications

(13 citation statements)

References 33 publications

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“…In the study [7], [8], an application assisted core assignment technique for the ARM big.LITTLE structure is proposed to save energy especially running the web browser. The study [9] proposes a technique for allocating CPU resources according to the concentration of users. For this purpose, one application is set to run in the foreground and others are set to run in the background.…”

Section: Related Workmentioning

confidence: 99%

Energy-Efficient Real-Time Multi-Core Assignment Scheme for Asymmetric Multi-Core Mobile Devices

Kim

Lim

2020

IEEE Access

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The big.LITTLE architecture has been extensively integrated into smart mobile devices for better performance and higher energy efficiency. However, the desired energy savings obtained by the big.LITTLE architecture is not sufficiently achieved because the LITTLE cores are not fully utilized while running real-time user applications. In this study, an energy efficient big.LITTLE core assignment algorithm is proposed to reduce the energy consumption of the mobile device by utilizing the LITTLE core as much as possible while guaranteeing the real-time performance of the mobile application. By applying the proposed multi-core assignment technique on a real test-bed of an off-the-shelf smartphone, we prove that the proposed technique can improve the energy saving effect while guaranteeing real-time performance. The energy efficiency of the proposed scheme is compared with that of the legacy scheduler in various environments. In addition, we propose a machine learning-based method to predict the expected processing time more accurately for a task before assigning to one of multi-cores. The presented prediction method is expected to reduce the chances of missing a deadline when employed on the proposed multi-core assignment scheme. INDEX TERMS energy conservation, asymmetric multi-cores, mobile devices, scheduling, real-time systems

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Section: Related Workmentioning

confidence: 99%

Energy-Efficient Real-Time Multi-Core Assignment Scheme for Asymmetric Multi-Core Mobile Devices

Kim

Lim

2020

IEEE Access

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“…Hsiu et al presented a scheduling technique for AMP‐style mobile processors. They note that the user's attention is primarily focused on the foreground app, and hence, large compute resources should be provided to it to ensure high QoE.…”

Section: Processor Architecture Level Techniquesmentioning

confidence: 99%

“…Objective Energy 3,10,13,14,16,17,24,27,30,[60][61][62][63] Webpage loading time 3,[8][9][10][11][12][13][14]16,17,[24][25][26][27][28][29][30][31]33,34,36,38,40,[43][44][45][46][47][48][49][50]51,52,[56][57][58]61,[64][65][66]…”

Section: Category Referencesunclassified

“…In this section, we discuss works that evaluate browser applications on different processor architectures (Section 6.1); propose DVFS, power gating, and asymmetric multicore scheduling (Section 6.2); perform intelligent scheduling of browser threads to cores (Section 6.3); and propose hardware customization (Section 6.4) and QoS abstractions (Section 6.5) for MWB. Power gating 46,50,52,59 Using extra hardware to operate on different properties of the ''style'' kernel in parallel 44 Thread/task scheduling [37][38][39][46][47][48][50][51][52]59 Basis of Thread/Task Scheduling Smart app (involving user interaction) vs maintenance functions 50,59 Critical (which impact PLT) vs noncritical threads 46,52 Long-lived vs short-lived threads 48 Task latency and/or deadline considerations [37][38][39]47 Power considerations 14,23 Thermal considerations 47 Functionalities of processing units 39 Consolidating threads on few cores to remove false parallelism 48…”

Section: Processor Architecture Level Techniquesmentioning

confidence: 99%

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A survey of techniques for improving efficiency of mobile web browsing

Mittal

Mattela

2019

Concurrency and Computation

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Summary Mobile web traffic has now surpassed the desktop web traffic and has become the primary means for service providers to reach out to the billions of end users. Due to this trend, optimization of mobile web browsing (MWB) has gained significant attention. In this paper, we present a survey of techniques for improving the efficiency of web browsing on mobile systems, proposed in the last 6‐7 years. We review the techniques from both the networking domain (eg, proxy and browser enhancements) and the processor architecture domain (eg, hardware customization and thread‐to‐core scheduling). We organize the research works based on key parameters to highlight their similarities and differences. Beyond summarizing the recent works, this survey aims to emphasize the need of architecting for MWB as the first principle, instead of retrofitting for it.

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“…In smartphone operating systems, the responsiveness perceived by the user is affected more by the foreground application with which the user is interacting than the applications that are running in the background. Therefore, several studies [3]- [7] have been conducted on limiting processor power consumption by assigning maximum processor resources to the foreground application and limiting resources to background applications. However, the previously proposed approaches have wasted energy by allocating more resources than the processor required to prevent performance degradation as perceived by the user.…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Control of Mobile Processors Based on Long Short-Term Memory

Lee

Park

2019

IEEE Access

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Smartphones that are equipped with high-clock frequency and multi-core processors are being commercially released to provide various services. As the number of cores and the clock speed of a mobile processor increases, its power consumption also increases, and several software approaches to reducing power consumption have been studied. Existing techniques estimate processor usage by measuring the processor usage at a previous time. However, these techniques often waste energy because they assign frequencies above the usage required to prevent degraded user responsiveness. Therefore, this paper proposes a machine learning method to predict the usage that the processor currently requires to prevent performance degradation while reducing power consumption. The proposed method is implemented through a processor power management system based on Long Short-Term Memory (LSTM). This system learns processor usage patterns in a variety of situations and predicts the processor usage required for the current situation. The number of computations required by the LSTM-based technique is analyzed according to the number of neurons and layers, and the computational load is then compared to an existing technique. Furthermore, a benchmarking tool that reflects the characteristics of mobile applications is used to test the performance of the proposed system, which is shown to reduce the power consumption of mobile processors by a maximum of 19% compared to the existing Android processor power management system.INDEX TERMS Energy management, dynamic voltage and frequency scaling, recurrent neural networks, mobile device.

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User-Centric Scheduling and Governing on Mobile Devices with big.LITTLE Processors

Cited by 19 publications

References 33 publications

Energy-Efficient Real-Time Multi-Core Assignment Scheme for Asymmetric Multi-Core Mobile Devices

Energy-Efficient Real-Time Multi-Core Assignment Scheme for Asymmetric Multi-Core Mobile Devices

A survey of techniques for improving efficiency of mobile web browsing

Energy-Efficient Control of Mobile Processors Based on Long Short-Term Memory

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