research-article

Personalized optimization for android smartphones

Authors:

Jihong KimAuthors Info & Claims

ACM Transactions on Embedded Computing Systems (TECS), Volume 13, Issue 2s

Article No.: 60, Pages 1 - 25

https://doi.org/10.1145/2544375.2544380

Published: 27 January 2014 Publication History

Abstract

As a highly personalized computing device, smartphones present a unique new opportunity for system optimization. For example, it is widely observed that a smartphone user exhibits very regular application usage patterns (although different users are quite different in their usage patterns). User-specific high-level app usage information, when properly managed, can provide valuable hints for optimizing various system design requirements. In this article, we describe the design and implementation of a personalized optimization framework for the Android platform that takes advantage of user's application usage patterns in optimizing the performance of the Android platform. Our optimization framework consists of two main components, the application usage modeling module and the usage model-based optimization module. We have developed two novel application usage models that correctly capture typical smartphone user's application usage patterns. Based on the application usage models, we have implemented an app-launching experience optimization technique which tries to minimize user-perceived delays, extra energy consumption, and state loss when a user launches apps. Our experimental results on the Nexus S Android reference phones show that our proposed optimization technique can avoid unnecessary application restarts by up to 78.4% over the default LRU-based policy of the Android platform.

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Cited By

Lim GKang DHam MEom YCosta XAl Hassanieh HAsadi ACox LPerino DWidmer JGiustiniano D(2023)SWAM: Revisiting Swap and OOMK for Improving Application Responsiveness on Mobile DevicesProceedings of the 29th Annual International Conference on Mobile Computing and Networking10.1145/3570361.3592518(1-15)Online publication date: 2-Oct-2023
https://dl.acm.org/doi/10.1145/3570361.3592518
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
Obradov AUsorac SAntic M(2021)Android runtime service optimization for execution inside LXC2021 44th International Convention on Information, Communication and Electronic Technology (MIPRO)10.23919/MIPRO52101.2021.9597181(995-997)Online publication date: 27-Sep-2021
https://doi.org/10.23919/MIPRO52101.2021.9597181
Show More Cited By

Index Terms

Personalized optimization for android smartphones
1. Software and its engineering
  1. Software creation and management
    1. Software development process management
      1. Software development methods

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Information & Contributors

Information

Published In

cover image ACM Transactions on Embedded Computing Systems

ACM Transactions on Embedded Computing Systems Volume 13, Issue 2s

Special Section ESFH'12, ESTIMedia'11 and Regular Papers

January 2014

409 pages

ISSN:1539-9087

EISSN:1558-3465

DOI:10.1145/2544375

Editors:
Naehyuck Chang
Seoul National University (SNU), Korea
,
Jian-Jia Chen
Karlsruhe Institute of Technology (KIT), Germany

Issue’s Table of Contents

Copyright © 2014 ACM.

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]

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Association for Computing Machinery

New York, NY, United States

Journal Family

ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 27 January 2014

Accepted: 01 November 2012

Revised: 01 September 2012

Received: 01 January 2012

Published in TECS Volume 13, Issue 2s

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Cited By

Lim GKang DHam MEom YCosta XAl Hassanieh HAsadi ACox LPerino DWidmer JGiustiniano D(2023)SWAM: Revisiting Swap and OOMK for Improving Application Responsiveness on Mobile DevicesProceedings of the 29th Annual International Conference on Mobile Computing and Networking10.1145/3570361.3592518(1-15)Online publication date: 2-Oct-2023
https://dl.acm.org/doi/10.1145/3570361.3592518
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
Obradov AUsorac SAntic M(2021)Android runtime service optimization for execution inside LXC2021 44th International Convention on Information, Communication and Electronic Technology (MIPRO)10.23919/MIPRO52101.2021.9597181(995-997)Online publication date: 27-Sep-2021
https://doi.org/10.23919/MIPRO52101.2021.9597181
Sarsenbayeva ZMarini Gvan Berkel NLuo CJiang WYang KWadley GDingler TKostakos VGoncalves JBernhaupt RMueller FVerweij DAndres JMcGrenere JCockburn AAvellino IGoguey ABjørn PZhao SSamson BKocielnik R(2020)Does Smartphone Use Drive our Emotions or vice versa? A Causal AnalysisProceedings of the 2020 CHI Conference on Human Factors in Computing Systems10.1145/3313831.3376163(1-15)Online publication date: 21-Apr-2020
https://dl.acm.org/doi/10.1145/3313831.3376163
Xin LFan HMa Z(2020)An Optimization of Memory Usage Based on the Android Low Memory Management MechanismsMobile Computing, Applications, and Services10.1007/978-3-030-64214-3_2(16-36)Online publication date: 19-Dec-2020
https://doi.org/10.1007/978-3-030-64214-3_2
Banerjee APaul HMukherjee ADey SDatta P(2020)A Framework for Speculative Job Scheduling on Mobile Cloud ResourcesBig Data Analytics for Cyber-Physical Systems10.1007/978-3-030-43494-6_4(103-128)Online publication date: 26-Jun-2020
https://doi.org/10.1007/978-3-030-43494-6_4
Yoon SYoun YBurgstaller BKim S(2019)Self-learnable Cluster-based Prefetching Method for DRAM-Flash Hybrid Main Memory ArchitectureACM Journal on Emerging Technologies in Computing Systems10.1145/328493215:1(1-21)Online publication date: 9-Jan-2019
https://dl.acm.org/doi/10.1145/3284932
Yoon SYoun YKim JKim S(2018)Design of DRAM-NAND flash hybrid main memory and Q-learning-based prefetching methodThe Journal of Supercomputing10.5555/3288339.328836274:10(5293-5313)Online publication date: 1-Oct-2018
https://dl.acm.org/doi/10.5555/3288339.3288362
Ved SAwasthi M(2018)Exploring non-volatile main memory architectures for handheld devices2018 Design, Automation & Test in Europe Conference & Exhibition (DATE)10.23919/DATE.2018.8342258(1528-1531)Online publication date: Mar-2018
https://doi.org/10.23919/DATE.2018.8342258
Lee SYoon SKim JKim S(2018)Adaptive correlated prefetch with large-scale hybrid memory system for stream processingThe Journal of Supercomputing10.1007/s11227-018-2466-774:9(4746-4770)Online publication date: 1-Sep-2018
https://dl.acm.org/doi/10.1007/s11227-018-2466-7
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