Users and Batteries : Interactions and Adaptive Power Management in Mobile Systems

1
Users and Batteries Interactions and Adaptive
Power Management in Mobile Systems

• Nilanjan Banerjee1, Ahmad Rahmati2, Mark Corner1,
• Sami Rollins3, Lin Zhong2

1University of Massachusetts, Amherst
2 Rice University
3University of San Francisco
http//prisms.cs.umass.edu/llama
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Scenario why did my laptop switch of ?

• You are riding a bus to work and you are five
  minutes away
• you are working on your laptop finishing a
  presentation
• Suddenly your laptop turns of ! Grrr !!!
• your laptop battery was running low
• You would have charged your laptop within 5
  minutes anyway
• you could have completed your presentation
  

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Scenario working on an airplane

• You are working on your presentation on a flight
  to Austria
• Midway through your flight your laptop turns of
• your battery could only last for three
  hours
• Wish your laptop adapted to your charging
  behavior !

4
Problem power management Vs user

• Power management for mobile systems are not
  user-centric
• do not adapt to changing user behavior and
  device modalities
• No understanding of how users use energy of their
  mobile device
• assumption users desire maximum lifetime out
  of batteries

User
Battery
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Solution energy for the user

• Understand user-battery interaction in mobile
  systems
• when, why and where do users recharge
• Built user-centric power management policy for
  mobile systems
• policy which adapts to varying
  user-battery behavior

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Outline

• User-study on laptops and mobile phone
• research methods for user-study
• Insights from the user study
• when, where, and why do users recharge
  batteries
• how predictable are recharge patterns
• User-centric power management
• design and implementation, and evaluation of
  Llama
• Related work
• Conclusions

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Study of user-battery interaction

• Goal examine where, when, and why people
  recharge
• subjects recruited from friends, family,
  mailing lists
• used three complimentary research methods

In-situ survey
Trace Collection
User Interviews
56 Laptops 15-150 days 10 Mobile phones
42-77 days
10 Laptops 10 Mobile phone age 20-26 years
10 Laptop 415 response 10 Mobile phone 91
responses
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Trace collection

• Goal collect quantitative records of battery
  level
• Laptop implementation is Java based
• runs on Microsoft Windows and Apple OS X
• records measurements periodically
• uploads data automatically to a central
  server once a day
• Mobile phone tool is written in C
• runs on Microsoft Windows Mobile
• tool distributed pre-installed on T-Mobile
  MDA phones
• aggressive wakes the phone very minute to
  take reading

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User interviews

• Gather qualitative data regarding user-battery
  interaction
• understand context of recharge
• Provided sample scenarios to participants to
  think about
• last time the user was faced with a low
  battery condition ?
• what impact did it have on their future
  behavior ?
• Questions about when, why, and where users
  recharge ?
• Encouraged users to tell their stories and
  anecdotes

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In-situ pop-up survey
Goal In-situ information about why users
recharge
Laptop
Mobile Phone
Disappears after a minute

• Filtered out intervals of less than 5 minutes
  between recharges

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Outline

• User-study on laptops and mobile phone
• research methods for user-study
• Insights from the user study
• when, where, and why do users recharge
  batteries
• how predictable are recharge patterns
• User-centric power management
• design and implementation, and evaluation of
  Llama
• Related work
• Conclusions

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Users have energy to spare
Laptops
50 of the recharges occur when the battery is
half full Fraction of users use their laptops
like desktops
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Users have energy to spare
Mobile Phones
60 of the recharges occur when the battery is
half full Most recharges occur between 25-75
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Recharges are context driven
Limited Opportunities Ahead
Limited Opportunities Ahead
System Reminder
System Reminder
Low Battery
Low Battery
Convenient location
Convenient Time
Convenient location
Convenient Time
Laptops
Mobile Phones
Fraction of recharges are driven by context
Low battery corresponded to 40 of the battery
remaining
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Variations across users and devices
Mobile Phones
Laptops
Variation in recharge pattern across mobile
phones and laptops Variation across recharge
patterns across users
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Summary of the user-study

• Recharges occur with significant energy remaining
  in batteries
• Charging is mostly driven by context and battery
  levels
• Users and devices show significant variation in
  battery usage
• power management should adapt with users
  and devices

I usually charge in the office when the indicator
shows 1 bar
I always recharge every night
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User-centric power management

• Users charge their system with significant
  battery left
• accurately predict excess energy left in
  the battery
• proactively use the remaining energy to
  improve QoS
• Optimization framework for power management
• maximize the excess energy usable by
  applications
• minimize the probability of running out of
  battery
• try to avoid true low battery levels

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Llama design and implementation

• Example Scenario
• Confidence of not exceeding battery capacity
  0.95
• Llama determines present battery percentage (Cp)
  30
• creates a histogram of recharges below Cp
  (H)
• Llama calculates 95 of the time user recharges
  by 10
• devote 10 to Llama application

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Llama applications and deployment
Screen Brightness excess energy to adjust
screen brightness

• Web prefetching
• prefetching a random webpage
• download interval determines aggressiveness

Health monitoring reports preprogrammed data
upload interval determines aggressiveness
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Llama deployment demographics
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Llama evaluation
Laptops
Mobile Phones
Llama used energy depending on battery left at
recharge Beneficial use of Llama more web
data, and brighter display
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Post-Llama recharge behavior
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Feedback loop with user
Recharge cycle becomes shorter and shorter,
frustrating the user Plan to address the problem
in future versions of Llama
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Post-Llama user study

• Interviews to evaluate negative effects of Llama
• impact of Llama on battery lifetime
• All mobile phone users but one showed similar
  satisfaction
• The battery lifetime was better last month, I
  have to recharge it every day now, but it used to
  be every day and a half

Laptop user
Even though I didnt notice it, I would
definitely care in situations where I require
maximum battery life
It must have been small, since I didnt notice it
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Future work

• Evaluate the positive effects of Llama
• what are the user-perceived benefits of
  Llama ?
• Improve the prediction algorithm of Llama
• use contextual information such as
  location, work patterns
• Experiment on different mobile devices like music
  players
• less biased or demographically weighted
  subject selection

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Related work

• MyExperience in-situ survey tool Mobisys 2007
• tool for in-situ profiling and survey
• Human factor in energy management
• user-interface design on energy efficiency
  Vallero et al.
• visual perception to reduce energy of LCDs
  Chen et al.
• Tools for studying mobile users in natural
  settings
• logging tool for studying HCI Demumieux
  et al.
• Balance performance and system-wide energy
  consumption
• Odyssey Flinn et al., Ecosystem Zeng
  et al.

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Conclusions

• First glimpse of user-battery interaction
• traces would be available through the
  traces.cs project
• User study produced three key observations
• users leave excess energy in the battery on
  recharge
• charging behavior is driven by opportunity and
  context
• significant variations across users and
  systems
• Built an user-centric energy management system
  called Llama
• it can scale energy usage to user behavior

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Users and Batteries Interactions and Adaptive
Power Management in Mobile Systems

• Nilanjan Banerjee1, Ahmad Rahmati2, Mark Corner1,
• Sami Rollins3, Lin Zhong2

1University of Massachusetts, Amherst
2 Rice University
3University of San Francisco
http//prisms.cs.umass.edu/llama
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HotMobile 2008
Napa, CA, February 25-26, 2008Submissions
October 16, 2007