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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. - PowerPoint PPT Presentation
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Diversity in Smartphone Usage
MobiSys ‘10June 17, 2010
UCLA, Microsoft, USC
Hossein Falaki, Ratul Mahajan, Srikanth KandulaDimitrios Lymberopoulos, Ramesh Govindan, Deborah Estrin
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2006 2007 2008 2009 2010 2011 2012 2013 20140
50
100
150
200
250
300
350
400
Western Europe Asia & Pacific
North America
(Source: Park Associates, 2009)
Smar
tpho
ne u
sers
(mill
ions
)
Smartphone Penetration Is on the Rise
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Basic Facts about Smartphone Usage Are Unknown
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Why Do We Need to Know These Facts?
How can we improve smartphone performance and usability?
Identical usersEveryone is different
?Can we improve resource management on
smartphones through personalization?
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Main Findings
1. Users are quantitatively very diverse in their usage
2. But invariants exist and can be harnessed
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Platform Demographics
Android 16 high school students17 knowledge workers
WinMobile 16 Social Communicators56 Life Power Users59 Business Power Users37 Organizer Practicals
Platform Information Logged
Android Screen stateApp usageBattery levelNet traffic per appCall starts and ends
WinMobile Screen stateApplications used
Data SetsPlatform # Users Duration
Android 33 7-21 Weeks/user
WinMobile 222 8-28 Weeks/user
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Diversity in interactionInteraction model
Diversity in application usageApplication usage model
Diversity in battery usageEnergy drain model
Outline
Comprehensive system view
Interaction Application Energy
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Users have disparate interaction levels
Two orders
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Sources of Interaction Diversity
1. User demographics2. Session count3. Session length4. Application use 5. Number of applications per session
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User Demographics Do Not Explain Diversity
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Session Lengths Contribute to Diversity
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Number of Sessions Contribute to Diversity
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Session Length and Count Are Uncorrelated
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Close Look at Interaction Sessions
Most sessions are short
Sessions terminated by screen timeout
Few very long sessions
Exponential distribution
Shifted Pareto distribution
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Modeling Interaction Sessions
Extremely long sessions are
being modeled well
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Implications of Interaction Diversity
• System parameters such as timeouts can be tuned based on model parameters
• System can be designed with insights from the distributions
Diversity Interaction Models
System Design Implications
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Diversity in application usageApplication usage model
Outline
Interaction
Application
Energy
Diversity in interactionInteraction model
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Users Run Disparate Number of Applications
50% of users run more than
40 apps
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Application Breakdown
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Close Look at Application PopularityStraight line in semi-log plot
appears for all users
Different list for each user
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Exponential Distribution Models App Popularity Well
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Implications of Application Diversity
• Most of a user’s attention is focused on a few applications• Optimize the system for the top applications for each user
Diversity Application Models
System Design Implications
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Diversity in application usageApplication usage model
Outline
Interaction
Application
Energy
Diversity in interactionInteraction model
Diversity in energy drainPredicting energy drain
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Users Are Diverse in Energy Drain
Two orders
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Close Look at Energy Drain
Significant variation across
time
High variation within each hour
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“Trend Table” Based Framework to Model Energy Drain
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Modeling Energy Drain
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Conclusions
Users are quantitatively diverse in their usage
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
