Improving application launch performance in smartphones using recurrent neural network

Andre Luiz Nunes Martins, Cesar A.V. Duarte, Jinkyu Jeong

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    Mobile phones became indispensable tools in our lives, withAndroid being the most used mobile OS. These devices depend onmanaging application lifecycles to improve launch performance,but the management of application processes is not done in anefficient way. The standard low memory killer, that is responsiblefor freeing memory, does not consider any user information and itfrequently kills applications that are going to be launched.Context-awareness presents several possibilities to make mobilesystems more efficient and user-driven in terms of user experience.In this paper, we introduce a context-based launcher usingrecurrent neural network (RNN), a special branch of neuralnetworks capable of remembering dependencies, taking inconsideration not just previous inputs, but also previous outputs,providing high accuracy without any extra sensor context. Oursystem guarantees that most of the applications are ready to use inthe background, substantially improving the launch time enablinga better user experience. Experimental results demonstrate that thenovel scheme can reduce application launch latency in asignificant manner.

    Original languageEnglish
    Title of host publication2018 International Conference on Machine Learning Technologies, ICMLT 2018
    PublisherAssociation for Computing Machinery
    Pages58-62
    Number of pages5
    ISBN (Electronic)9781450364324
    DOIs
    Publication statusPublished - 2018 May 19
    Event2018 International Conference on Machine Learning Technologies, ICMLT 2018 - Jinan, China
    Duration: 2018 May 262018 May 28

    Publication series

    NameACM International Conference Proceeding Series

    Conference

    Conference2018 International Conference on Machine Learning Technologies, ICMLT 2018
    Country/TerritoryChina
    CityJinan
    Period18/5/2618/5/28

    Bibliographical note

    Funding Information:
    This research was supported partly by Next-Generation Information Computing Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (NRF-2016M3C4A7952587) and partly by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MISP) (NRF-2017R1C1B2007273).

    Publisher Copyright:
    © 2018 Association for Computing Machinery.

    All Science Journal Classification (ASJC) codes

    • Software
    • Human-Computer Interaction
    • Computer Vision and Pattern Recognition
    • Computer Networks and Communications

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