skip to main content
10.1145/2307636.2307648acmconferencesArticle/Chapter ViewAbstractPublication PagesmobisysConference Proceedingsconference-collections
research-article

Fast app launching for mobile devices using predictive user context

Published:25 June 2012Publication History

ABSTRACT

As mobile apps become more closely integrated into our everyday lives, mobile app interactions ought to be rapid and responsive. Unfortunately, even the basic primitive of launching a mobile app is sorrowfully sluggish: 20 seconds of delay is not uncommon even for very popular apps.

We have designed and built FALCON to remedy slow app launch. FALCON uses contexts such as user location and temporal access patterns to predict app launches before they occur. FALCON then provides systems support for effective app-specific prelaunching, which can dramatically reduce perceived delay.

FALCON uses novel features derived through extensive data analysis, and a novel cost-benefit learning algorithm that has strong predictive performance and low runtime overhead. Trace-based analysis shows that an average user saves around 6 seconds per app startup time with daily energy cost of no more than 2% battery life, and on average gets content that is only 3 minutes old at launch without needing to wait for content to update. FALCON is implemented as an OS modification to the Windows Phone OS.

References

  1. C. C. Aggarwal, J. L. Wolf, and P. S. Yu. Caching on the world wide web. IEEE Transactions on Knowledge and Data Engineering, 11(1):95--107, 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Apple - batteries - iphone. http://www.apple.com/batteries/iphone.html, 2011.11.28.Google ScholarGoogle Scholar
  3. D. Chu, A. Kansal, J. Liu, and F. Zhao. Mobile apps: it's time to move up to condos. In 13th USENIX conference on Hot topics in Operating Systems (HotOS), pages 16--16, Napa, California, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Daily tip: How to make your iphone camera launch instantly {jailbreak}. http://www.tipb.com/2011/04/20/daily-tip-iphone-camera-launch-instantly-jailbreak/, 2011.4.20.Google ScholarGoogle Scholar
  5. D. Garlan, D. P. Siewiorek, and P. Steenkiste. Project aura: Toward distraction-free pervasive computing. IEEE Pervasive Computing, 1:22--31, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. iOS 5 slowing iPhone 4 and 4S complaints. http://www.phonesreview.co.uk/2011/10/25/ios-5-slowing-iphone-4-and-4s-complaints/, 2011.10.25.Google ScholarGoogle Scholar
  7. Y. Joo, J. Ryu, S. Park, and K. G. Shin. Fast: Quick application launch on solid-state drives. In 9th USENIX Conference on File and Storage Technologies, San Jose, CA, USA, pages 259--272. USENIX, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Link prefetching. https://developer.mozilla.org/en/link_prefetching_faq.Google ScholarGoogle Scholar
  9. D. Lymberopoulos, O. Riva, K. Strauss, A. Mittal, and A. Ntoulas. Pocketweb: instant web browsing for mobile devices. In Proceedings of the 17th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), pages 1--12, London, UK, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. H. J. Miller and J. Han. Geographic Data Mining and Knowledge Discovery. Taylor & Francis, Inc., Bristol, PA, USA, 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. F. Qian, Z. Wang, A. Gerber, Z. M. Mao, S. Sen, and O. Spatscheck. Profiling resource usage for mobile applications: a cross-layer approach. In Proceedings of the 9th International Conference on Mobile Systems, Applications, and Services (MobiSys 2011), pages 321--334, Bethesda, MD, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. M. Satyanarayanan. Pervasive computing: Vision and challenges. IEEE Personal Communications, 8:10--17, 2001.Google ScholarGoogle ScholarCross RefCross Ref
  13. C. Shepard, A. Rahmati, C. Tossell, L. Zhong, and P. Kortum. Livelab: measuring wireless networks and smartphone users in the field. SIGMETRICS Performance Evaluation Review, (3):15--20, Jan. 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Tasker. http://tasker.dinglisch.net/.Google ScholarGoogle Scholar
  15. Y. Zhu and D. Shasha. Efficient elastic burst detection in data streams. In Proceedings of the ninth ACM international conference on Knowledge discovery and data mining (SIGKDD), pages 336--345, New York, NY, USA, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Fast app launching for mobile devices using predictive user context

      Recommendations

      Comments

      Login options

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

      Sign in
      • Published in

        cover image ACM Conferences
        MobiSys '12: Proceedings of the 10th international conference on Mobile systems, applications, and services
        June 2012
        548 pages
        ISBN:9781450313018
        DOI:10.1145/2307636

        Copyright © 2012 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]

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 25 June 2012

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • research-article

        Acceptance Rates

        Overall Acceptance Rate274of1,679submissions,16%

        Upcoming Conference

        MOBISYS '24

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader