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CONTEXT-AWARE BATTERY MAN-AGEMENT FOR MOBILE PHONES (PERCOM 08)

이상훈 , 오교중2009. 12. 07

Nishkam Ravi, James Scott, Lu Han and Liviu Iftode

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Contents

Introduction Problem definition System design Evaluation Conclusion Pros

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Introduction

Mobile devices are providing increasing functionality due to rapid improvements

However, battery capacities are not im-proved as other technologies Energy will remain the main bottleneck in

the future

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Introduction

Current battery management Informed to decide prioritization of the tasks

Battery meter “battery low” audio signals Remaining time estimate at current power

The user get into habit of charging at suit-able period Based on their call patterns

Low-power standby modes Accustomed to the users

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Introduction

Factors to change current battery man-agement Multi-functional computing expects always-

on WLAN are hungry consumer of energy Pervasive computing asks to be always-on

for background applications These battery consumptions

Require the user to charge more frequently Break the low standby-mode power profile

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Introduction

Goal Propose a new context-aware battery man-

agement architecture for mobile devices (CABMAN)

Three principles Crucial applications (telephony) should not

be compromised by non-crucial applications Charging opportunities should be predicted Context can be used to predict charging

opportunities

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Problem Definition

Will the phone battery last until the next charging opportunity is encountered? When the next opportunity for recharging

the battery will be available? If then what is the total battery lifetime available

to the user? What fraction of this battery lifetime will be

consumed by critical applications such as telephony?

What fraction of this battery lifetime can be left for use by noncritical applications?

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Problem Definition

To build a system that can monitor user context and sense the battery charge level of the device, it requires A set of algorithms for making predictions A central component for assimilating the in-

formation together and warning the user appropriately

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System Design

Eight components Three categories

System specific monitors Predictors Viceroy/UI

Figure 1. CABMAN system architec-ture

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System-specific Compo-nents Detect various data from the OS

Battery status By battery monitor

List and status of processes By process monitor

Call logs By call monitor

Context information to predict next charging oppor-tunity By context monitor

Separated from the OS to facilitate porting of CABMAN to the multiple platforms

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Charging Opportunity Predictor

Determine the charging opportunity is soon enough for battery

Should provide right information Warn with high battery level if the charging

opportunity is low and vice versa Use location sensing by GPS

To infer charging opportunity Limited usage (still many devices don’t sup-

port) Only respect to static charging opportunities

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Charging Opportunity Predictor

Cell based charging opportunity prediction algo-rithm Used with following information

Location Cell ID of connected phone

Chosen cells Marked as being charging opportunities

Expected time to reach those cells Prediction by pattern-matching against larger historical set of

cell movement patterns Current pattern is by using a number of samples being the

current and most recent cell ids Historical set is history of a number of days of cell movement

patterns

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Charging Opportunity Predictor

Charging opportunity prediction algo-rithm Based on current sample (ABC) Search patterns including sample (DE-

ABCFG) between entry of the current cell and the

next charging capable cell Average time to provide prediction

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Call Time Predictor

Prevent other application to drain the battery for “crucial application” (telephony)

Three options Ask the user to set a minimum call time level Use past calling behavior to find the call time average

Find upper bound of used need of each day Enhanced by compute weekdays and weekends separately

Hybrid approach “keep twice my average call time available, and a mini-

mum of 10 minutes for emergencies in addition to the pre-dicted call time”

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Battery Lifetime Predictor

Monitor drain rate of the battery Accurate estimation with same battery con-

sumption level But some are very over time

Different from battery age Many don’t replace it

Propose a battery lifetime metric Independent of battery age Considering application’s battery usage

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Battery Lifetime Predictor

Figure 2. A new laptop Figure 3. An old lap-top

Figure 4. HP iPAQ

Base curve of battery discharge

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Battery Lifetime Predictor

Measure “discharge speedup factor” Measure the battery capacity c1 and c2 at two

time instances t1 and t2 with application run-ning

Measure the battery capacity c1 and c2 at tow time instances t3 and t4 on idle state(base curve)

Calculated as (t4 –t3)/(t2-t1) Divide the remaining lifetime of the battery

by the discharge speedup factor to obtain the predicted remaining time for the battery

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Viceroy and User Interface

Continually monitor the battery lifetime predic-tion will expire before the next charging opportu-nity If then, notify the user using the UI

When informed the user Kill some battery-hungry applications Make their behavior consume less power Plan to charge device according to the timescale

from the viceroy Sacrifice crucial applications If the user is at a place of charging opportunity

Ask user to charge directly

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Evaluation

Charging-opportunity predictor Call-time predictor Battery time predictor

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Charging Opportunity Predictor

History set of MIT’s Reality Mining project 80 users, 9months

Varying parameters: sample size, history size Increasing sample size generally increases

accuracy and reliability Sample size of 10 bottomed up 40 days of historical data is optimal

User behavior changes Average prediction error is 16%, 12 minutes

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Charging Opportunity Predictor

Figure 4. Charging opportunity prediction error for various sample sizes and history sizes

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Call Time Predictor

Average prediction error is under a minute out of the hour Typical call is shot (90% are lees than 5

minutes) Very few calls in a typical hour(75% with 2

calls or fewer) Cannot predict a “long tail”

Occasionally long incoming calls Try to preserve applications of telephony

for emergencies, rendezvous

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Call Time Predictor

Figure 5. Absolute call time prediction error for weekdays (top) and weekends (bottom)

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Call Time Predictor

Figure 6. CDF of the length of phone calls (Left) and the number of calls made during each hour (Right)

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Battery-lifetime Predictor

Based on base curves With new and old batteries A set of applications

Web, music and video By comparing

Actual consumption Advanced Configuration and Power Interface

(ACPI) Estimation of the discharge speedup factor

Showed better prediction than ACPI

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Battery-lifetime Predictor

Figure 7. Base curve together with discharge curves for the new HP laptop (Left) and old Dell laptop (Right)

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Battery-lifetime Predictor

Figure 8. Base curve together with dis-charge curves (actual and derived) for HPiPAQ

Table 1. comparing accuracy of algorithm with ACPI’s

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Discussion

Relatively more accurate prediction for average user whose life entropy is not very high.

Additional context information will be needed to improve the accuracy. Calendar information, information about the

travel plans of the user, charge-logs, etc.

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Conclusion

Describe three key components of CAB-MAN: The use of context information such as lo-

cation to predict the next charging oppor-tunity

More accurate battery life prediction based on a discharge speedup factor

The notion of crucial applications such as telephony

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Conclusion

Evaluation Test results are very positive Charging opportunity prediction exhibiting

an average error of 12 minutes Battery life prediction having average er-

rors of between 4 and 12 minutes Call time prediction algorithm has average

errors measured in seconds “minimum call time remaining”

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Pros (literature level)

논문 구조가 복잡하지 않아 전반적으로 이해하기 쉬움

해결하고자 하는 문제가 이해하기 쉽게 설명됨

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Pros (System level)

Battery management 를 위해 각각의 예측 알고리즘을 적용한 점이 돋보임

Preliminary research 이기 때문에 각각의 predictor 및 시스템이 어떻게 구현될지는 구체적으로 알 수는 없지만 , 일부분 (battery lifetime predictor) 은 feasible 함 다른 predictor 는 좀 더 feasible 해야 함

여러 모바일 기기에서 사용 가능하도록 sys-tem non-specific 한 접근이 돋보임