research-article

Every Byte Counts: Selective Prefetching for Mobile Applications

Authors:

Silvia SantiniAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 1, Issue 2

Article No.: 6, Pages 1 - 29

https://doi.org/10.1145/3090052

Published: 30 June 2017 Publication History

Abstract

Quick responses to user actions are instrumental to the success of mobile applications. To ensure such responsiveness, applications often prefetch data objects before the user requests them. This way, applications can avoid the need to retrieve data through slow network connections during user interactions. However, prefetches may also harm. They increase launch delays and might cause substantial amounts of data to be downloaded through energy-hungry, cellular connections. In this paper, we propose EBC, a novel algorithm to schedule application prefetches and overcome their drawbacks. EBC computes application usage probabilities and traffic volume estimates to determine when and for which applications prefetches should be triggered. Thereby, it applies different strategies depending on whether a cellular or Wi-Fi connection is available. We evaluate the performance of EBC on two publicly available, large-scale data sets: LiveLab and Device Analyzer. Our results show that EBC can lower launch delays and ensure freshness of application content. At the same time, it reduces the amount of data downloaded through cellular connections. On the Device Analyzer data set, for instance, EBC achieves a 10% reduction in cellular traffic and a 36% better average freshness with respect to its closest competitor.

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Cited By

Callan JPetke J(2025)Multi-objective improvement of Android applicationsAutomated Software Engineering10.1007/s10515-024-00472-732:1Online publication date: 1-Jun-2025
https://dl.acm.org/doi/10.1007/s10515-024-00472-7
Shen ZYang KZhao XZou JDu W(2023)DeepAPP: A Deep Reinforcement Learning Framework for Mobile Application Usage PredictionIEEE Transactions on Mobile Computing10.1109/TMC.2021.309361922:2(824-840)Online publication date: 1-Feb-2023
https://doi.org/10.1109/TMC.2021.3093619
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https://dl.acm.org/doi/10.1145/3498361.3538929
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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 1, Issue 2

June 2017

665 pages

EISSN:2474-9567

DOI:10.1145/3120957

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Copyright © 2017 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 the author(s) 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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Publication History

Published: 30 June 2017

Accepted: 01 May 2017

Revised: 01 April 2017

Received: 01 February 2017

Published in IMWUT Volume 1, Issue 2

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Cited By

Callan JPetke J(2025)Multi-objective improvement of Android applicationsAutomated Software Engineering10.1007/s10515-024-00472-732:1Online publication date: 1-Jun-2025
https://dl.acm.org/doi/10.1007/s10515-024-00472-7
Shen ZYang KZhao XZou JDu W(2023)DeepAPP: A Deep Reinforcement Learning Framework for Mobile Application Usage PredictionIEEE Transactions on Mobile Computing10.1109/TMC.2021.309361922:2(824-840)Online publication date: 1-Feb-2023
https://doi.org/10.1109/TMC.2021.3093619
Ramanujam MChen HMardani SNetravali RBulusu NAryafar EBalasubramanian ASong J(2022)FlooProceedings of the 20th Annual International Conference on Mobile Systems, Applications and Services10.1145/3498361.3538929(168-182)Online publication date: 27-Jun-2022
https://dl.acm.org/doi/10.1145/3498361.3538929
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
Ramanujam MMadhyastha HNetravali RBanerjee SMottola LZhou X(2021)MarauderProceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services10.1145/3458864.3466866(350-362)Online publication date: 24-Jun-2021
https://dl.acm.org/doi/10.1145/3458864.3466866
Zhao YYin SSejfia ALaser MWang HMedvidovic N(2021)Assessing the Feasibility of Web-Request Prediction Models on Mobile Platforms2021 IEEE/ACM 8th International Conference on Mobile Software Engineering and Systems (MobileSoft)10.1109/MobileSoft52590.2021.00008(12-23)Online publication date: May-2021
https://doi.org/10.1109/MobileSoft52590.2021.00008
Callan JPetke J(2021)Improving Android App Responsiveness Through Automated Frame Rate ReductionSearch-Based Software Engineering10.1007/978-3-030-88106-1_10(136-150)Online publication date: 29-Sep-2021
https://doi.org/10.1007/978-3-030-88106-1_10
Lall SMoravapalle USivakumar RToni LBegen AAlay ÖTimmerer C(2020)MANTISProceedings of the 11th ACM Multimedia Systems Conference10.1145/3339825.3391864(112-125)Online publication date: 27-May-2020
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Choi BKim JCho DKim SHan DDimitropoulos XDainotti AVanbever LBenson T(2018)APPxProceedings of the 14th International Conference on emerging Networking EXperiments and Technologies10.1145/3281411.3281416(27-40)Online publication date: 4-Dec-2018
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Zhao YWat PLaser MMedvidović NHuchard MKästner CFraser G(2018)Empirically assessing opportunities for prefetching and caching in mobile appsProceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering10.1145/3238147.3238215(554-564)Online publication date: 3-Sep-2018
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