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GreenHub: a large-scale collaborative dataset to battery consumption analysis of android devices

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Abstract

Context

The development of solutions to improve battery life in Android smartphones and the energy efficiency of apps running on them is hindered by diversity. There are more than 24k Android smartphone models in the world. Moreover, there are multiple active operating system versions, and a myriad application usage profiles.

Objective

In such a high-diversity scenario, profiling for energy has only limited applicability. One would need to obtain information about energy use in real usage scenarios to make informed, effective decisions about energy optimization. The goal of our work is to understand how Android usage, apps, operating systems, hardware, and user habits influence battery lifespan.

Method

We leverage crowdsourcing to collect information about energy in real-world usage scenarios. This data is collected by a mobile app, which we developed and made available to the public through Google Play store, and periodically uploaded to a centralized server and made publicly available to researchers, app developers, and smartphone manufacturers through multiple channels (SQL, REST API, zipped CSV/Parquet dump).

Results

This paper presents the results of a wide analysis of the tendency several smart-phone characteristics have on the battery charge/discharge rate, such as the different models, brands, networks, settings, applications, and even countries. Our analysis was performed over the crowdsourced data, and we have presented findings such as which applications tend to be around when battery consumption is the highest, do users from different countries have the same battery usage, and even showcase methods to help developers find and improve energy inefficient processes. The dataset we considered is sizable; it comprises 23+ million (anonymous) data samples stemming from a large number of installations of the mobile app. Moreover, it includes 700+ million data points pertaining to processes running on these devices. In addition, the dataset is diverse. It covers 1.6k+ device brands, 11.8k+ smartphone models, and more than 50 Android versions. We have been using this dataset to perform multiple analyses. For example, we studied what are the most common apps running on these smartphones and related the presence of those apps in memory with the battery discharge rate of these devices. We have also used this dataset in teaching, having had students practicing data analysis and machine learning techniques for relating energy consumption/charging rates with many other hardware and software qualities, attributes and user behaviors.

Conclusions

The dataset we considered can support studies with a wide range of research goals, be those energy efficiency or not. It opens the opportunity to inform and reshape user habits, and even influence the development of both hardware (manufacturers) and software (developers) for mobile devices. Our analysis also shows results which go outside of the common perception of what impacts battery consumption in real-world usage, while exposing new varied, complex, and promising research avenues.

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Notes

  1. https://developer.android.com/topic/performance/power/

  2. https://developer.android.com/guide/topics/location/battery

  3. https://developer.android.com/docs/quality-guidelines/building-for-billions-battery-consumption

  4. https://greenhubproject.org/

  5. https://github.com/greenhub-project

  6. https://play.google.com/store/apps/details?id=com.hmatalonga.greenhub

  7. The remainder accounts for battery changing by 2% or more

  8. www2.cin.ufpe.br/site/lerNoticia.php?s=1&c=94&id=1697

  9. www.visao.sapo.pt/actualidade/sociedade/2017-10-11-Bateria-do-telemovel-invista-agora-para-poupar-depois

  10. https://www.publico.pt/2017/10/09/tecnologia/noticia/desenvolvida-aplicacao-para-poupar-bateria-de-dispositivos-moveis-1788153

  11. www.90segundosdeciencia.pt/episodes/ep-443-joao-paulo-fernandes/

  12. www.rtp.pt/play/p2063/e342304/ponto-de-partida

  13. The dataset does not include any data collected during development and testing of the BatteryHub application

  14. https://farmer.greenhubproject.org/

  15. https://docs.greenhubproject.org/api/getting-started.html

  16. https://docs.greenhubproject.org/api-reference/

  17. The dataset includes samples collected prior to that date, but they correspond to the infrastructure testing period.

  18. https://farmer.greenhubproject.org/storage/dataset.7z

  19. http://parquet.apache.org

  20. https://farmer.greenhubproject.org/storage/dataset.parquet.7z

  21. https://laravel.com/

  22. In fact, as we will discuss in detail in Section 6, this number includes installations of a clone which was made of BatteryHub, which at least up to a certain version, contributed with data to the dataset.

  23. Google app: https://play.google.com/store/apps/details?id=com.google.android.googlequicksearchbox

  24. Facebook app: https://play.google.com/store/apps/details?id=com.facebook.katana

  25. Messenger app: https://play.google.com/store/apps/details?id=com.facebook.orca

  26. AndroidRank: https://www.androidrank.org/

  27. Instagram app: https://play.google.com/store/apps/details?id=com.instagram.android

  28. Our GreenHub Pipeline notebooks are detailed in Section 7. The notebooks can be found at https://github.com/greenhub-project/notebooks

  29. https://unstats.un.org/unsd/mi/worldmillennium.htm

  30. https://unstats.un.org/unsd/mi/ldc.htm

  31. https://www.nytimes.com/2017/11/24/technology/personaltech/facebook-battery-drain.html

  32. It is common that, when such components have a low data throughput, the device automatically puts them under a low level of usage or even idle state, limiting its capacity but saving resources.

  33. Can be one of the other 3, but the Android API could not identify which one

  34. http://berkeleyearth.org/data/

  35. https://datahelpdesk.worldbank.org/knowledgebase/articles/902061-climate-data-api

  36. https://unstats.un.org/unsd/mi/worldmillennium.htm

  37. https://unstats.un.org/unsd/mi/ldc.htm

  38. https://developer.android.com/preview

  39. https://www.devicespecifications.com/

  40. Battery Double: https://play.google.com/store/apps/details?id=com.mansoon.BatteryDouble&hl=en

  41. Fabric is a usage and crashlytics reporter: https://get.fabric.io/

  42. DownloadAPK: https://downloadapk.net/Battery-Double.html

  43. APKPure: https://apkpure.com/br/battery-analytics/com.mansoon.BatteryDouble/versions?fbclid=IwAR2gmTl73T17fRsSg1vpnJdUqapSFoAbFUGQ7eV6IfcriOnljx_rqAoLpQA

  44. AppBrain: https://www.appbrain.com/app/battery-analytics/com.mansoon.BatteryDouble

  45. Jadx: https://github.com/skylot/jadx

  46. APKtool: https://ibotpeaches.github.io/Apktool/

  47. Number of samples required to obtain a confidence level of 95% and a confidence interval of ± 3, considering each dataset as the population.

  48. Vegan: https://cran.r-project.org/web/packages/vegan/index.html

  49. Dataset-Converter Tool: https://github.com/greenhub-project/dataset-converter

  50. Pandas: https://pandas.pydata.org/

  51. Project Jupyter: https://jupyter.org/

  52. Apache Parquet: https://parquet.apache.org/

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Acknowledgements

This research and work is funded: by national funds through the FCT - Foundation for Science and Technology, I.P., within the scope of the project CISUC - UID/CEC/00326/2020, project UIDB/50014/2020, and by European Social Fund, through the Regional Operational Program Centro 2020; by operation Centro-01-0145-FEDER-000019 - C4 - Centro de Competências em Cloud Computing, co-financed by the European Regional Development Fund (ERDF) through the Programa Operacional Regional do Centro (Centro 2020), in the scope of the Sistema de Apoio à Investigação Científica e Tecnológica - Programas Integrados de IC&DT; by CNPq/Brazil (304755/2014-1, 406308/2016-0), FACEPE/Brazil (APQ-0839-1.03/14), and INES 2.0, FACEPE grants PRONEX APQ 0388-1.03/14 and APQ-0399-1.03/17, and CNPq grant 465614/2014-0; by NOVA LINCS (UIDB/04516/2020) with the financial support of FCT - Foundation for Science and Technology; The first author was financed by post-doc grant reference C4_SMDS_L1-1_D and the third author financed by FCT grant SFRH/BD/132485/2017; Additionally, this paper acknowledges the support of the Erasmus+ Key Action 2 (Strategic partnership for higher education) project No. 2020-1-PT01-KA203-078646: “SusTrainable - Promoting Sustainability as a Fundamental Driver in Software Development Training and Education”. The information and views set out in this paper are those of the author(s) and do not necessarily reflect the official opinion of the European Union. Neither the European Union institutions and bodies nor any person acting on their behalf may be held responsible for the use which may be made of the information contained therein.

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Authors and Affiliations

  1. C4 - Centro de Competências em Cloud Computing (C4-UBI), Universidade da Beira Interior, Rua Marquês d’Ávila e Bolama, Covilhã, 6201-001, Portugal

    Rui Pereira

  2. HASLab/INESC Tec, Porto, Portugal

    Rui Pereira & Marco Couto

  3. Universidade do Minho, Braga, Portugal

    Hugo Matalonga & Marco Couto

  4. Universidade Federal de Pernambuco, Recife, Brasil

    Fernando Castor

  5. CISUC, DEI, Universidade de Coimbra, Coimbra, Portugal

    Bruno Cabral, Pedro Carvalho & João Paulo Fernandes

  6. Universidade da Beira Interior, C4, Lab. Release, NOVA-LINCS, Covilhã, Portugal

    Simão Melo de Sousa

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  1. Rui Pereira
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  2. Hugo Matalonga
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  4. Fernando Castor
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  7. Simão Melo de Sousa
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  8. João Paulo Fernandes
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Correspondence to Rui Pereira.

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Communicated by: Yasutaka Kamei, Andy Zaidman

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Pereira, R., Matalonga, H., Couto, M. et al. GreenHub: a large-scale collaborative dataset to battery consumption analysis of android devices. Empir Software Eng 26, 38 (2021). https://doi.org/10.1007/s10664-020-09925-5

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