context-aware battery management for mobile phones (percom 08) 이상훈, 오교중 2009. 12. 07 1...
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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