1University of Massachusetts,

Amherst

Users and Batteries : Interactions and Adaptive Power Management in

Mobile Systems

Nilanjan Banerjee1, Ahmad Rahmati2, Mark Corner1,

Sami Rollins3, Lin Zhong2

2 Rice University3University of San

Francisco

http://prisms.cs.umass.edu/llama

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

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

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

Battery

User

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

user behavior

energy management

OutlineUser-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 interactionGoal : examine where, when, and why people recharge

subjects recruited from friends, family, mailing lists

used three complimentary research methods

10 Laptops10 Mobile phone age 20-26 years

10 Laptop 415 response10 Mobile phone 91 responses

56 Laptops 15-150 days10 Mobile phones 42-77 days

Trace Collection User Interviews In-situ survey

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

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

Filtered out intervals of less than 5 minutes between recharges

Disappears after a minute

Laptop

Mobile Phone

Goal: In-situ information about why users recharge

OutlineUser-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

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

3 13 23 33 43 53 63 73 83 93

Percentage at which battery is recharged

Pe

rce

nta

ge

of

rec

ha

rge

s Laptops

50% of the recharges occur when the battery is half full

Fraction of users use their laptops like desktops

Users have energy to spare

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

3 13 23 33 43 53 63 73 83 93

Percentage at which battery is recharges

Pe

rce

nta

ge

of

rec

ha

rge

s

Mobile Phones

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

Most recharges occur between 25-75 %

Recharges are context driven

Convenient location

Convenient location

System Reminde

r

System Reminde

r

Convenient Time

Convenient Time

Low BatteryLow Battery

Limited Opportunitie

s Ahead

Limited Opportunitie

s Ahead

Laptops

Mobile Phones

Fraction of recharges are driven by context

Low battery corresponded to 40% of the battery remaining

Variations across users and devices

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20

30

40

50

60

70

80

90

100

User 1 User 2 User 3 User 4 User 5

Bat

tery

Lev

el o

n R

echa

rge

Mobile Phones

Variation in recharge pattern across mobile phones and laptops

Variation across recharge patterns across users

Laptops

0

10

20

30

40

50

60

70

80

90

100

User 1 User 2 User 3 User 4 User 5

Ba

tte

ry L

ev

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on

Re

ch

arg

e

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 always recharge every night

I usually charge in the office when the indicator shows 1 bar

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

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

Histogram ofRecharges

Histogram ofRecharges belowpresent capacity

Energy-adaptivealgorithm

Present BatteryCapacity

Energy forLlama app

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 deployment demographics

Particulars Laptop Mobile Phone

Application Screen brightness Web prefetching

Health monitoring

Subjects 2 females , 8 male20-30 years

1 female, 9 males20-30 years

Number of Days 30 30

Llama evaluation

0

2

4

6

8

10

12

0 1 2 3 4 5 6 7 8 9

User ID

Pe

rce

nta

ge

Ba

tte

ry U

se

d b

y L

lam

a

0

5

10

15

20

25

0 1 2 3 4 5 6 7 8

User ID

Pe

rce

nta

ge

Ba

tte

ry U

se

d b

y L

lam

a

Laptops Mobile Phones

Llama used energy depending on battery left at recharge

Beneficial use of Llama more web data, and brighter display

Post-Llama recharge behavior

Particulars Laptop Mobile Phone

Number of recharges (per week)

Pre-Llama = 6.5Post-Llama = 7.8

Pre-Llama = 10.1Post-Llama = 8.9

Recharges below 5%

Pre-Llama = 1%Post-Llama = 1%

Pre-Llama = 4%Post-Llama = 7%

Feedback loop with user

User recharges at afixed percentage

Llama used upexcess energy

Recharge cyclebecomes shorter

Recharge cycle becomes shorter and shorter, frustrating the user

Plan to address the problem in future versions of Llama

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”

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

Even though I didn’t notice it, I would definitely care in situations where I require maximum battery life

Laptop user

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 workMyExperience 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

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

1University of Massachusetts,

Amherst

Users and Batteries : Interactions and Adaptive Power Management in

Mobile Systems

Nilanjan Banerjee1, Ahmad Rahmati2, Mark Corner1,

Sami Rollins3, Lin Zhong2

2 Rice University3University of San

Francisco

http://prisms.cs.umass.edu/llama

HotMobile 2008

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

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