research-article

Why application errors drain battery easily?: a study of memory leaks in smartphone apps

Authors:

Mingyuan Xia,

Wenbo He,

Xue Liu,

Jie LiuAuthors Info & Claims

HotPower '13: Proceedings of the Workshop on Power-Aware Computing and Systems

Article No.: 2, Pages 1 - 5

https://doi.org/10.1145/2525526.2525846

Published: 03 November 2013 Publication History

Get Access

Abstract

Mobile operating systems embrace new mechanisms that reduce energy consumption for common usage scenarios. The background app design is a representative implemented in all major mobile OSes. The OS keeps apps that are not currently interacting with the user in memory to avoid repeated app loading. This mechanism improves responsiveness and reduces the energy consumption when the user switches apps. However, we demonstrate that application errors, in particular memory leaks that cause system memory pressure, can easily cripple this mechanism. In this paper, we conduct experiments on real Android smartphones to 1) evaluate how the background app design improves responsiveness and saves energy; 2) characterize memory leaks in Android apps and outline its energy impact; 3) propose design improvements to retrofit the mechanism against memory leaks.

References

[1]

J.d. power and associates reports: Smartphone battery life has become a significant drain on customer satisfaction and loyalty. https://pictures.dealer.com/jdpower/166f0d180a0d02b7014443191870cdac.pdf.

Google Scholar

[2]

Bond, M. D., and McKinley, K. S. Bell: bit-encoding online memory leak detection. In ASPLOS'06.

Digital Library

Google Scholar

[3]

Bond, M. D., and McKinley, K. S. Tolerating memory leaks. In OOPSLA '08.

Digital Library

Google Scholar

[4]

Carroll, A., and Heiser, G. An analysis of power consumption in a smartphone. In USENIX ATC'10.

Digital Library

Google Scholar

[5]

Hauswirth, M., and Chilimbi, T. M. Low-overhead memory leak detection using adaptive statistical profiling. In ASPLOS'04.

Digital Library

Google Scholar

[6]

Jump, M., and McKinley, K. S. Cork: dynamic memory leak detection for garbage-collected languages. In POPL '07.

Digital Library

Google Scholar

[7]

Mitchell, N., and Sevitsky, G. Leakbot: An automated and lightweight tool for diagnosing memory leaks in large java applications. In ECOOP'03.

Google Scholar

[8]

Pathak, A., Hu, Y. C., and Zhang, M. Bootstrapping energy debugging on smartphones: a first look at energy bugs in mobile devices. In HotNets'11.

Digital Library

Google Scholar

[9]

Pathak, A., Jindal, A., Hu, Y. C., and Midkiff, S. P. What is keeping my phone awake?: characterizing and detecting no-sleep energy bugs in smartphone apps. In MobiSys '12.

Digital Library

Google Scholar

[10]

Shepard, C., Rahmati, A., Tossell, C., Zhong, L., and Kortum, P. Livelab: measuring wireless networks and smartphone users in the field. SIGMETRICS Perform. Eval. Rev. (2011).

Digital Library

Google Scholar

[11]

Tang, Y., Gao, Q., and Qin, F. Leaksurvivor: towards safely tolerating memory leaks for garbage-collected languages. In USENIX ATC'08.

Digital Library

Google Scholar

[12]

Wang, Z., Lin, F. X., Zhong, L., and Chishtie, M. How far can client-only solutions go for mobile browser speed? In WWW '12.

Digital Library

Google Scholar

[13]

Yan, T., Chu, D., Ganesan, D., Kansal, A., and Liu, J. Fast app launching for mobile devices using predictive user context. In MobiSys '12.

Digital Library

Google Scholar

Cited By

View all

Rahaman SFarooq UNeamtiu IZhao Z(2023)Detecting Potential User-data Save & Export Losses due to Android App Termination2023 IEEE/ACM International Conference on Automation of Software Test (AST)10.1109/AST58925.2023.00019(152-162)Online publication date: May-2023
https://doi.org/10.1109/AST58925.2023.00019
Shanbhag SChimalakonda SSharma VKaulgud V(2022)Towards a Catalog of Energy Patterns in Deep Learning DevelopmentProceedings of the 26th International Conference on Evaluation and Assessment in Software Engineering10.1145/3530019.3530035(150-159)Online publication date: 13-Jun-2022
https://dl.acm.org/doi/10.1145/3530019.3530035
Hort MKechagia MSarro FHarman M(2022)A Survey of Performance Optimization for Mobile ApplicationsIEEE Transactions on Software Engineering10.1109/TSE.2021.307119348:8(2879-2904)Online publication date: 1-Aug-2022
https://doi.org/10.1109/TSE.2021.3071193
Show More Cited By

Recommendations

Smartphone Energy Drain in the Wild: Analysis and Implications
SIGMETRICS '15: Proceedings of the 2015 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems

The limited battery life of modern smartphones remains a leading factor adversely affecting the mobile experience of millions of smartphone users. In order to extend battery life, it is critical to understand where and how is energy drain happening on ...
Smartphone Energy Drain in the Wild: Analysis and Implications
Performance evaluation review

The limited battery life of modern smartphones remains a leading factor adversely affecting the mobile experience of millions of smartphone users. In order to extend battery life, it is critical to understand where and how is energy drain happening on ...
Characterizing and modeling the impact of wireless signal strength on smartphone battery drain
SIGMETRICS '13: Proceedings of the ACM SIGMETRICS/international conference on Measurement and modeling of computer systems

Despite the tremendous market penetration of smartphones, their utility has been and will remain severely limited by their battery life. A major source of smartphone battery drain is accessing the Internet over cellular or WiFi connection when running ...

Comments

Information & Contributors

Information

Published In

HotPower '13: Proceedings of the Workshop on Power-Aware Computing and Systems

November 2013

66 pages

ISBN:9781450324588

DOI:10.1145/2525526

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 November 2013

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Research-article

Funding Sources

Natural Sciences and Engineering Research Council of Canada

Conference

SOSP '13

Sponsor:

SIGOPS

SOSP '13: ACM SIGOPS 24th Symposium on Operating Systems Principles

November 3 - 6, 2013

Pennsylvania, Farmington

Acceptance Rates

HotPower '13 Paper Acceptance Rate 13 of 38 submissions, 34%;

Overall Acceptance Rate 20 of 50 submissions, 40%

Upcoming Conference

SOSP '24

Sponsor:
sigops

ACM SIGOPS 30th Symposium on Operating Systems Principles

November 5 - 8, 2024

Austin , TX , USA

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

10
Total Citations
View Citations
492
Total Downloads

Downloads (Last 12 months)17
Downloads (Last 6 weeks)4

Reflects downloads up to 10 Aug 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Rahaman SFarooq UNeamtiu IZhao Z(2023)Detecting Potential User-data Save & Export Losses due to Android App Termination2023 IEEE/ACM International Conference on Automation of Software Test (AST)10.1109/AST58925.2023.00019(152-162)Online publication date: May-2023
https://doi.org/10.1109/AST58925.2023.00019
Shanbhag SChimalakonda SSharma VKaulgud V(2022)Towards a Catalog of Energy Patterns in Deep Learning DevelopmentProceedings of the 26th International Conference on Evaluation and Assessment in Software Engineering10.1145/3530019.3530035(150-159)Online publication date: 13-Jun-2022
https://dl.acm.org/doi/10.1145/3530019.3530035
Hort MKechagia MSarro FHarman M(2022)A Survey of Performance Optimization for Mobile ApplicationsIEEE Transactions on Software Engineering10.1109/TSE.2021.307119348:8(2879-2904)Online publication date: 1-Aug-2022
https://doi.org/10.1109/TSE.2021.3071193
Pramanik PSinhababu NMukherjee BPadmanaban SMaity AUpadhyaya BHolm-Nielsen JChoudhury P(2019)Power Consumption Analysis, Measurement, Management, and Issues: A State-of-the-Art Review of Smartphone Battery and Energy UsageIEEE Access10.1109/ACCESS.2019.29586847(182113-182172)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2958684
Rosario Espada Adel Mar Gallardo MSalmerón APanizo LMerino P(2019)A formal approach to automatically analyse extra‐functional properties in mobile applicationsSoftware Testing, Verification and Reliability10.1002/stvr.169929:4-5Online publication date: 14-Jun-2019
https://doi.org/10.1002/stvr.1699
Gao YDong WHuang HBu JChen CXia MLiu X(2017)Whom to Blame? Automatic Diagnosis of Performance Bottlenecks on SmartphonesIEEE Transactions on Mobile Computing10.1109/TMC.2016.260425816:6(1773-1785)Online publication date: 1-Jun-2017
https://dl.acm.org/doi/10.1109/TMC.2016.2604258
Zhang HWu HRountev ABudnik CFraser GLonetti F(2016)Automated test generation for detection of leaks in Android applicationsProceedings of the 11th International Workshop on Automation of Software Test10.1145/2896921.2896932(64-70)Online publication date: 14-May-2016
https://dl.acm.org/doi/10.1145/2896921.2896932
Espada AMar Gallardo MSalmerón AMerino P(2015)Runtime Verification of Expected Energy Consumption in SmartphonesProceedings of the 22nd International Symposium on Model Checking Software - Volume 923210.1007/978-3-319-23404-5_10(132-149)Online publication date: 24-Aug-2015
https://dl.acm.org/doi/10.1007/978-3-319-23404-5_10
Baik KHuh J(2014)Balanced memory management for smartphones based on adaptive background app managementThe 18th IEEE International Symposium on Consumer Electronics (ISCE 2014)10.1109/ISCE.2014.6884413(1-2)Online publication date: Jun-2014
https://doi.org/10.1109/ISCE.2014.6884413
Andrade EMachida F(undefined)Assuring Autonomy of UAVs in Mission-critical Scenarios by Performability Modeling and AnalysisACM Transactions on Cyber-Physical Systems10.1145/3624572
https://dl.acm.org/doi/10.1145/3624572

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Recommendations

Smartphone Energy Drain in the Wild: Analysis and Implications

Smartphone Energy Drain in the Wild: Analysis and Implications

Characterizing and modeling the impact of wireless signal strength on smartphone battery drain

Comments

Published In

Sponsors

Publisher

Publication History

Permissions

Check for updates

Qualifiers

Funding Sources

Conference

Acceptance Rates

Upcoming Conference

Other Metrics

Article Metrics

Other Metrics

Cited By

Login options

Full Access

PDF

eReader

Abstract

References

Cited By

Recommendations

Smartphone Energy Drain in the Wild: Analysis and Implications

Smartphone Energy Drain in the Wild: Analysis and Implications

Characterizing and modeling the impact of wireless signal strength on smartphone battery drain

Comments

Information

Published In

Sponsors

Publisher

Publication History

Permissions

Check for updates

Qualifiers

Funding Sources

Conference

Acceptance Rates

Upcoming Conference

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Get Access

Login options

Full Access

View options

PDF

eReader

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations