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Diversity in Smartphone Usage. Hossein Falaki , Ratul Mahajan , Srikanth Kandula Dimitrios Lymberopoulos , Ramesh Govindan , Deborah Estrin. UCLA, Microsoft, USC. MobiSys ‘10 June 17, 2010. Smartphone Penetration Is on the Rise. Basic Facts about Smartphone Usage Are Unknown.

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Diversity in Smartphone Usage


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    1. Diversity in Smartphone Usage • HosseinFalaki, RatulMahajan, SrikanthKandula • DimitriosLymberopoulos, RameshGovindan, Deborah Estrin • UCLA, Microsoft, USC MobiSys ‘10 June 17, 2010

    2. Smartphone Penetration Is on the Rise

    3. Basic Facts about Smartphone Usage Are Unknown

    4. Why Do We Need to Know These Facts? How can we improve smartphone performance and usability? Identical users Everyone is different ? Can we improve resource management on smartphones through personalization?

    5. Main Findings 1. Users are quantitatively very diverse in their usage 2. But invariants exist and can be harnessed

    6. Data Sets

    7. Outline • Comprehensive • system view • Diversity in interaction • Interaction model • Diversity in application usage • Application usage model • Diversity in battery usage • Energy drain model • Interaction • Application • Energy

    8. Users have disparate interaction levels Two orders

    9. Sources of Interaction Diversity • User demographics • Session count • Session length • Application use • Number of applications per session

    10. User Demographics Do Not Explain Diversity

    11. Session Lengths Contribute to Diversity

    12. Number of Sessions Contribute to Diversity

    13. Session Length and Count Are Uncorrelated

    14. Close Look at Interaction Sessions Sessions terminated by screen timeout Exponential distribution Few very long sessions Most sessions are short Shifted Pareto distribution

    15. Modeling Interaction Sessions Extremely long sessions are being modeled well

    16. Implications of Interaction Diversity Diversity Interaction Models System Design Implications • System parameters such as timeouts can be tuned based on model parameters • System can be designed with insights from the distributions

    17. Outline • Diversity in application usage • Application usage model • Interaction • Application • Energy • Diversity in interaction • Interaction model

    18. Users Run Disparate Number of Applications 50% of users run more than 40 apps

    19. Application Breakdown

    20. Close Look at Application Popularity Straight line in semi-log plot appears for all users Different list for each user

    21. Exponential Distribution Models App Popularity Well

    22. Implications of Application Diversity Diversity Application Models System Design Implications • Most of a user’s attention is focused on a few applications • Optimize the system for the top applications for each user

    23. Outline • Diversity in application usage • Application usage model • Interaction • Application • Energy • Diversity in interaction • Interaction model • Diversity in energy drain • Predicting energy drain

    24. Users Are Diverse in Energy Drain Two orders

    25. Close Look at Energy Drain High variation within each hour Significant variation across time

    26. “Trend Table” Based Framework to Model Energy Drain

    27. Modeling Energy Drain

    28. Conclusions • Building effective systems for all users is challenging • Static policies cannot work well for all users Users are quantitatively diverse in their usage Invariants exist and can be harnessed • Users have similar distributions with different parameters. • This significantly facilitates the adaptation task

    29. Diversity in Smartphone Usage • HosseinFalaki • falaki@cs.ucla.edu MobiSys ‘10 June 17, 2010