Adaptive and Flexible Smartphone Power Modeling

Nacci, Alessandro Antonio; Trovò, Francesco; Maggi, F.; Ferroni, Matteo; Cazzola, Andrea; Sciuto, D.; Santambrogio, Marco D.

doi:10.1007/s11036-013-0470-y

Cited by 19 publications

(4 citation statements)

References 19 publications

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“…eir approach is specifically aimed at testing Android applications. Nacci et al [10] introduced an approach to build a power model for Android devices by using Android APIs to retrieve a variety of states, including the battery, network connection, Wi-Fi, and screen. Two components usually implement the models:…”

Section: Phase IImentioning

confidence: 99%

Methodological Guidelines for Measuring Energy Consumption of Software Applications

Ardito

Coppola

Morisio

et al. 2019

Scientific Programming

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Energy consumption information for devices, as available in the literature, is typically obtained with ad hoc approaches, thus making replication and consumption data comparison difficult. We propose a process for measuring the energy consumption of a software application. The process contains four phases, each providing a structured deliverable that reports the information required to replicate the measurement. The process also guides the researcher on a threat to validity analysis to be included in each deliverable. This analysis ensures better reliability, trust, and confidence to reuse the collected consumption data. Such a process produces a structured consumption data for any kind of electronic device (IoT devices, mobile phones, personal computers, servers, etc.), which can be published and shared with other researchers fostering comparison or further investigations. A real case example demonstrates how to apply the process and how to create the required deliverables.

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Section: Phase IImentioning

confidence: 99%

Methodological Guidelines for Measuring Energy Consumption of Software Applications

Ardito

Coppola

Morisio

et al. 2019

Scientific Programming

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“…Reference [10] develops a power model based on user activity for an Android-based smartphone, using regression techniques. Work in [11,12] estimates power consumption through adaptive modeling based on monitored performance activity, and integrate it in the MPower app, which provides the user suggestions to improve power efficiency. MPower collects measures on the target device and transmit them to a server for power estimation.…”

Section: Related Workmentioning

confidence: 99%

BLAST: Battery Lifetime-constrained Adaptation with Selected Target in Mobile Devices

Mercati

Hanumaiah

Kulkarni

et al. 2015

Proceedings of the 12th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services

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Mobile devices today contain many power hungry subsystems and execute different applications. Standard power management is not aware of the desired battery lifetime and has no visibility into which applications are executing. However, power consumption is strongly dependent on which applications are executed. In this work, we propose a novel power characterization strategy for mobile devices called application-dependent power states (AP-states). Based on that, we formulate a management problem to improve performance under battery lifetime constraints, and we implement the management framework on a real Android device. We call our framework BLAST: Battery Lifetime-constrained Adaptation with Selected Target. The goal of such framework is to maximize performance while letting the device battery to last at least for a certain required lifetime, and only requires the user to select the desired target lifetime. The implementation does not require OS modifications and can be ported and installed to any Android device. We experimentally verify that our strategy can still meets user experience requirements with a selected target battery lifetime extension of at least 25%.

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“…We validated the aforementioned approach in a mobile device scenario: we developed an Android application, codename MPower (available on the Google Play Store), to collect data about smartphones and tablet in their real usage scenarios [11,17]. The system produced power models for more than one thousand of devices, showing how the proposed methodology is able to perform better than the one implemented by Google Historian for recent releases of the Android operating system, thus allowing the definition of more effective power management policies able to save as much power as possible with respect to the user's goals.…”

Section: The Mpower Case Studymentioning

confidence: 99%

Power-awareness and smart-resource management in embedded computing systems

Santambrogio¹,

Ayala²,

Campanoni³

et al. 2015

2015 International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS)

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Resources such as quantities of transistors and memory, the level of integration and the speed of components have increased dramatically over the years. Even though the technologies have improved, we continue to apply outdated approaches to our use of these resources. Key computer science abstractions have not changed since the 1960's. Therefore this is the time for a fresh approach to the way systems are designed and used.

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Adaptive and Flexible Smartphone Power Modeling

Cited by 19 publications

References 19 publications

Methodological Guidelines for Measuring Energy Consumption of Software Applications

Methodological Guidelines for Measuring Energy Consumption of Software Applications

BLAST: Battery Lifetime-constrained Adaptation with Selected Target in Mobile Devices

Power-awareness and smart-resource management in embedded computing systems

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