users and batteries interactions and adaptive power management in mobile systems l.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Users and Batteries : Interactions and Adaptive Power Management in Mobile Systems PowerPoint Presentation
Download Presentation
Users and Batteries : Interactions and Adaptive Power Management in Mobile Systems

Loading in 2 Seconds...

play fullscreen
1 / 29

Users and Batteries : Interactions and Adaptive Power Management in Mobile Systems - PowerPoint PPT Presentation


  • 474 Views
  • Uploaded on

Users and Batteries : Interactions and Adaptive Power Management in Mobile Systems Nilanjan Banerjee 1 , Ahmad Rahmati 2 , Mark Corner 1 , Sami Rollins 3 , Lin Zhong 2 1 University of Massachusetts, Amherst 2 Rice University 3 University of San Francisco

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Users and Batteries : Interactions and Adaptive Power Management in Mobile Systems' - Audrey


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
users and batteries interactions and adaptive power management in mobile systems

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

scenario why did my laptop switch of
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
scenario working on an airplane
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 !
problem power management vs user
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

solution energy for the user
Solution: energy for the user

energy management

user behavior

  • 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
outline
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
study of user battery interaction
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

trace collection
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
user interviews
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
in situ pop up survey
In-situ pop-up survey
  • Filtered out intervals of less than 5 minutes between recharges

Goal: In-situ information about why users recharge

Laptop

Mobile Phone

Disappears after a minute

outline11
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
users have energy to spare
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

users have energy to spare13
Users have energy to spare

Mobile Phones

60% of the recharges occur when the battery is half full

Most recharges occur between 25-75 %

recharges are context driven
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

Mobile Phones

Laptops

Fraction of recharges are driven by context

Low battery corresponded to 40% of the battery remaining

variations across users and devices
Variations across users and devices

Laptops

Mobile Phones

Variation in recharge pattern across mobile phones and laptops

Variation across recharge patterns across users

summary of the user study
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

user centric power management
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
llama design and implementation
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
llama applications and deployment
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

llama evaluation
Llama evaluation

Laptops

Mobile Phones

Llama used energy depending on battery left at recharge

Beneficial use of Llama

more web data, and brighter display

feedback loop with user
Feedback loop with user

Recharge cycle becomes shorter and shorter, frustrating the user

Plan to address the problem in future versions of Llama

post llama user study
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 didn’t notice it, I would definitely care in situations where I require maximum battery life

It must have been small, since I didn’t notice it

future work
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
related work
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.]
conclusions
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
users and batteries interactions and adaptive power management in mobile systems28

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

hotmobile 2008
HotMobile 2008

Napa, CA, February 25-26, 2008Submissions: October 16, 2007