Detecting display energy hotspots in Android apps

Wan, Mian; Jin, Yuchen; Li, Ding; Gui, Jiaping; Mahajan, Sonal; Halfond, William G. J.

doi:10.1002/stvr.1635

Cited by 23 publications

(6 citation statements)

References 70 publications

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“…When checking the "Keep screen on" option, the profiler visual component, implemented via an Android activity, is set with the WindowManager.LayoutParams.FLAG_KEEP_SCREEN_ON flag to keep the device screen turned on while the profiling procedure described earlier is performed. Providing that screen energy consumption varies with pixel colors, 34 we display a background that mixes three energy-efficient colors and three energy-inefficient colors to reflect an average energy consumption. The resulting picture can be observed on the right of Figure 11.…”

Section: Device Profiler: Battery-depletion Trace Generatormentioning

confidence: 99%

DewSim: A trace‐driven toolkit for simulating mobile device clusters in Dew computing environments

et al. 2019

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Summary Dew computing is an emerging computing paradigm, which aims at minimizing the dependency over existing internetwork back‐haul, ie, being dependent on processing resources offered by remote servers. Smartphones and tablets ubiquity and powerful computing hardware motivated researchers to investigate the way of providing Dew computing services by exploiting the aggregated capabilities of devices in a vicinity, a smart device cluster. Consequently, research on resource management is necessary to learn how to scavenge resources from such a cluster, deal with devices heterogeneity, limitations, and dynamic resource availability. Simulation is commonly practiced for studying resource management in other distributed computing research fields, specially due to the complexity involved in the set up of experiments. However, a free‐to‐use purpose specific toolkit for studying smart device clusters do not exist or have been documented. Current simulation efforts do not allow researchers to faithfully represent key singularities of such environment, which are energy depletion and nondedicated nature of computing resources. We propose a trace‐based toolkit built on modular software artifacts to speed up research in resource management techniques in Dew environments. A trace‐driven methodology is adopted to assure practical value of simulated scenarios. The toolkit comprises a device profiler application for Android to capture generic battery and CPU traces from real devices, a profile mixer to create user interaction baseline traces through generic ones, and an extensible engine to simulate the execution of workloads configurable via text files. Verification and validation tests were run to show correctness and reliability of our simulation approach.

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Section: Device Profiler: Battery-depletion Trace Generatormentioning

confidence: 99%

DewSim: A trace‐driven toolkit for simulating mobile device clusters in Dew computing environments

et al. 2019

View full text Add to dashboard Cite

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“…Since energy consumption of OLED displays depends on the color displayed by each pixel, application UI color selections became an important aspect for optimizing energy consumption of the application. Therefore, researchers create display power models and develop automatic color transformation techniques to help developers making energy efficient applications through UI color selections(eg, previous studies [26][27][28][29][30] ).…”

Section: Related Workmentioning

confidence: 99%

Supporting software evolution through feedback on executing/skipping energy tests for proposed source code changes

Sahin

Pollock

Clause

2019

J Software Evolu Process

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With the increasing use of battery‐powered devices comes the need to test mobile applications for energy consumption and energy issues. Unfortunately, energy testing is expensive because it is a manual, labor‐intensive process that often requires multiple, separate, energy‐measuring devices to collect energy usage data. The high costs of energy testing can negatively affect the planning process of application evolution. For example, developers might be limited in the number of changes they can include in a release because they must conservatively plan to conduct energy testing after each change. In this paper, we present a new approach to provide developers with feedback on executing/skipping energy tests for proposed code changes. Our technique leverages change impact analysis and precomputed API energy usage information. More specifically, for a proposed change, the technique predicts whether energy testing will be required, and if so, which energy tests will need to be run. Such information may allow developers to avoid spending unnecessary time for energy testing and develop an effective application evolution timeline. To investigate the feasibility of our technique, we implemented a prototype for Android applications and conducted three case studies at different granularity levels on 10 Android applications.

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“…Similarly, Manotas et al (2014) developed a set of recommendations to support developers in coding more energy aware apps. Wan et al (2015) presented a technique to detect display energy hotspots of mobile device apps, reporting them to developers.…”

Section: Related Workmentioning

confidence: 99%

An app performance optimization advisor for mobile device app marketplaces

Saborido

Khomh

Hindle

et al. 2018

Sustainable Computing: Informatics and Systems

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On mobile phones, users and developers use apps official marketplaces serving as repositories of apps. The Google Play Store and Apple Store are the official marketplaces of Android and Apple products which offer more than a million apps. Although both repositories offer description of apps, information concerning performance is not available. Due to the constrained hardware of mobile devices, users and developers have to meticulously manage the resources available and they should be given access to performance information about apps. Even if this information was available, the selection of apps would still depend on user preferences and it would require a huge cognitive effort to make optimal decisions. Considering this fact we propose APOA, a recommendation system which can be implemented in any marketplace for helping users and developers to compare apps in terms of performance.APOA uses as input metric values of apps and a set of metrics to optimize. It solves an optimization problem and it generates optimal sets of apps for different user's context. We show how APOA works over an Android case study. Out of 140 apps, we define typical usage scenarios and we collect measurements of power, CPU, memory, and network usages to demonstrate the benefit of using APOA.

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Detecting display energy hotspots in Android apps

Cited by 23 publications

References 70 publications

DewSim: A trace‐driven toolkit for simulating mobile device clusters in Dew computing environments

DewSim: A trace‐driven toolkit for simulating mobile device clusters in Dew computing environments

Supporting software evolution through feedback on executing/skipping energy tests for proposed source code changes

An app performance optimization advisor for mobile device app marketplaces

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