Diversity in Smartphone Usage Hossein Falaki, Ratul mahajan, Srikanth kandula, Dimitrios Lymberopoulous, Ramesh Govindan, Deborah Estrin. UCLA, Microsoft, USC MobiSys ‘10 Presented by Vignesh Saravanaperumal

Smart phone - Intro Mobile phone Smart phone (Mobile phone + various Sensors ) What if your monitor could be plugged into your phone? What if you really didn't need a laptop, since your phone's CPU could power most applications, and draw data from the cloud? Nirvana Phones

What does this Paper say? This paper, in short is kind of statistics paper which discusses about the various ways the users interact with the smart phones and its outcome

Why did they do this paper?

Basic Facts about Smartphone Usage Are Unknown 5

Why Do We Need to Know These Facts? 6 How can we improve smart phone performance and usability? Identical users Everyone is different Can we improve resource management on smart phones through personalization?

Main Findings 7 1. Users are quantitatively very diverse in their usage 2. But invariants exist and can be harnessed

Smart phone Usage  Diversity in interaction  Interaction model  Diversity in application usage  Application usage model  Diversity in battery usage  Energy drain model 8 Comprehensive system view Interaction ApplicationEnergy

Who participated in this survey? PlatformDemographics Android  16 high school students  17 knowledge workers WinMobile  16 Social Communicators  56 Life Power Users  59 Business Power Users  37 Organizer Practicals PlatformInformation Logged Android  Screen state  App usage  Battery level  Net traffic per app  Call starts and ends WinMobile  Screen state  Applications used 9 Platform# UsersDuration Android Weeks/user WinMobile Weeks/user

Users have disparate interaction levels 10 Two orders

Sources of Interaction Diversity User Demographics Session count Session length 11

User Demographics Do Not Explain Diversity Interaction Time:

Session Lengths Contribute to Diversity 13

Number of Sessions Contribute to Diversity 14

Session Length and Count Are Uncorrelated Interaction Sessions:

Close Look at Interaction Sessions 16 Most sessions are short Sessions terminated by screen timeout Few very long sessions Exponential distribution Shifted Pareto distribution

Modeling Interaction Sessions 17 Extremely long sessions are being modeled well

Diurnal Patterns:

Smart phone Usage  Diversity in application usage  Application usage model 19 Interaction Application Energy  Diversity in interaction  Interaction model

Users Run Disparate Number of Applications 20 50% of users run more than 40 apps

Application Breakdown:

Close Look at Application Popularity 22 Straight line in semi-log plot appears for all users Different list for each user

Application Popularity Relationship to user demographic: What does this graph signifies? These graphs cannot reliably predict how a user will use the phone. While demographic information appears to help in some cases (for e.g., the variation in usage of productivity software in Dataset1), such cases are not the norm, and it is hard to guess when demographic information would be useful.

Diurnal patterns: Time dependent application popularity was recently reported by Trestian based on an analysis of the network traffic logs from a 3G provider and this analysis confirms the effect.

Application Sessions Applications run per interaction: 90%, of interactions include only one application

Application session lengths: what interesting sight do these graphs reveal ?

Smart phone Usage  Diversity in application usage  Application usage model 27 Interaction Application Energy  Diversity in interaction  Interaction model  Diversity in energy drain  Predicting energy drain

Users Are Diverse in Energy Drain 28 Two orders

Close Look at Energy Drain 29 Significant variation across time High variation within each hour

Modeling Energy Drain 30

Network Traffic  Traffic per day  Interactive traffic  Diurnal patterns The Network Analysis was carried out on Dataset 1 The traffic includes 3G radio and the wireless link

Network Traffic Traffic per day: The traffic received - 1 to 1000 MB The traffic sent to 100 MB. The median values are 30 MB sent and 5 MB received

Conclusions Users are quantitatively diverse in their usage 33 Invariants exist and can be harnessed Building effective systems for all users is challenging Static policies cannot work well for all users Users have similar distributions with different parameters. This significantly facilitates the adaptation task

Questions Raised? Based upon these statistics what can be the solution to Resource management in Smart phones? Customization (Adaptation), but Is it possible?  Analyzing the Qualitative similarities among users  User behavior in the past must also predictive of the future