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Practical prediction and prefetch for faster access to applications on mobile phones

Published: 08 September 2013 Publication History

Abstract

Mobile phones have evolved from communication devices to indispensable accessories with access to real-time content. The increasing reliance on dynamic content comes at the cost of increased latency to pull the content from the Internet before the user can start using it. While prior work has explored parts of this problem, they ignore the bandwidth costs of prefetching, incur significant training overhead, need several sensors to be turned on, and do not consider practical systems issues that arise from the limited background processing capability supported by mobile operating systems. In this paper, we make app prefetch practical on mobile phones. Our contributions are two-fold. First, we design an app prediction algorithm, APPM, that requires no prior training, adapts to usage dynamics, predicts not only which app will be used next but also when it will be used, and provides high accuracy without requiring additional sensor context. Second, we perform parallel prefetch on screen unlock, a mechanism that leverages the benefits of prediction while operating within the constraints of mobile operating systems. Our experiments are conducted on long-term traces, live deployments on the Android Play Market, and user studies, and show that we outperform prior approaches to predicting app usage, while also providing practical ways to prefetch application content on mobile phones.

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  • (2024)An Adaptive Android Memory Management Based on a Lightweight PSO-LSTM Model2024 IEEE Wireless Communications and Networking Conference (WCNC)10.1109/WCNC57260.2024.10570952(1-6)Online publication date: 21-Apr-2024
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cover image ACM Conferences
UbiComp '13: Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
September 2013
846 pages
ISBN:9781450317702
DOI:10.1145/2493432
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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Published: 08 September 2013

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Author Tags

  1. app prediction
  2. mobile computing
  3. prefetch

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UbiComp '13 Paper Acceptance Rate 92 of 394 submissions, 23%;
Overall Acceptance Rate 764 of 2,912 submissions, 26%

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Cited By

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  • (2024)MAPLEProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435148:1(1-25)Online publication date: 6-Mar-2024
  • (2024)MemSaver: Enabling an All-in-memory Switch Experience for Many Apps in a SmartphoneProceedings of the 15th ACM/SPEC International Conference on Performance Engineering10.1145/3629526.3645050(267-275)Online publication date: 7-May-2024
  • (2024)An Adaptive Android Memory Management Based on a Lightweight PSO-LSTM Model2024 IEEE Wireless Communications and Networking Conference (WCNC)10.1109/WCNC57260.2024.10570952(1-6)Online publication date: 21-Apr-2024
  • (2024)BERT-Based Semantic-Aware Heterogeneous Graph Embedding Method for Enhancing App Usage Prediction AccuracyIEEE Transactions on Human-Machine Systems10.1109/THMS.2024.341227354:4(465-474)Online publication date: Aug-2024
  • (2024)ChameSC: Virtualizing Superscalar Core of a SIMD Architecture for Vector Memory Access2024 IEEE 42nd International Conference on Computer Design (ICCD)10.1109/ICCD63220.2024.00019(52-59)Online publication date: 18-Nov-2024
  • (2024)Enhancing App Usage Prediction Accuracy With GCN-Transformer Model and Meta-Path ContextIEEE Access10.1109/ACCESS.2024.337239712(53031-53044)Online publication date: 2024
  • (2024)Social media use is predictable from app sequencesComputers in Human Behavior10.1016/j.chb.2024.108381161:COnline publication date: 1-Dec-2024
  • (2024)Caching in Location Based Services: Approaches, Challenges and Emerging TrendsWireless Personal Communications: An International Journal10.1007/s11277-024-11132-0135:3(1581-1615)Online publication date: 1-Apr-2024
  • (2024)Federated privacy-preserving collaborative filtering for on-device next app predictionUser Modeling and User-Adapted Interaction10.1007/s11257-024-09395-034:4(1369-1398)Online publication date: 28-Mar-2024
  • (2023)Fast application launch on personal computing/communication devicesProceedings of the 21st USENIX Conference on File and Storage Technologies10.5555/3585938.3585965(425-439)Online publication date: 21-Feb-2023
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