1. Energy
Management
for
Mobile
Devices:
Power
Es8ma8on
Technique
for
Modern
Smartphones
연세대학교 컴퓨터과학과
모바일 임베디드 시스템 연구실
윤찬민 (cmyoon@yonsei.ac.kr)
DEVIEW
2013
2013.10.14

2. Mobile
Pla@orms
FM
Radio
Proximity
GPS
Camera
Light
Mic.
Accel
Compass
Gyroscope Thermometer
Barometer
Gesture
Feature
Phone
GSM
240x320
Display
Cellular Bluetooth
Galaxy
S
Galaxy
S2
Galaxy
S3
1GHz
Single
core
GSM/HSDPA
480x800
Display,
4.0
inch
1500
mAh
baWery
1.2GHz
Dual
core
GSM/HSDPA
480x800
Display,
4.3
inch
1650
mAh
baWery
1.4GHz
Quad
core
GSM/HSDPA/LTE
720x1280
Display,
4.8
inch
2100
mAh
baWery
WiFi
Galaxy
S4
1.6GHz
Octa
core
GSM/WCDMA/LTE
1080x1920
Display(Full
HD),
5.0
inch
2600
mAh
baWery
NFC
IrDA
Mobile
Embedded
System
Lab.,
Yonsei
University
2

3. Energy
Management
for
Mobile
Devices
𝛼
Mobile Embedded System Lab., Yonsei University
3

4. Energy
Management
Techniques
Application; Energy Anomaly;
Energy Bug; Energy Hog; Energy
Leakage; Wakelock; Non-Sleep;
Anomaly Detection; Debugging;
Anomaly Reporting, …
Battery Lifetime; User
Interaction; Requirement; User
Experience; Personalization;
Quality of Service; User Context;
Spatiotemporal Context, …
• 단말/응용 가용시간 예측
• 사용자 요구 및 컨텍스트 반영
• 사용자중심 Energy-aware UX
• 응용프로그램의 전력 소모 특성
정보 수집 관리 기술
• 가상 배터리 관리 기법
• Energy-aware OS
사용자
시스템 소프
트웨어
Energy Usage; Application
Energy Estimation; Process
Energy Estimation;
Virtualization; Battery
Segmentation; Resource
Management, …
응용
프로그램
• 실시간 응용프로그램 에너지 bug/
hog 감지
• 응용프로그램의 에너지 특성에
따른 에너지 bug 및 hog 원인 분
석 및 리포팅 시스템
• 하드웨어 전력프로파일링 및 모델링
• 하드웨어 컴포넌트 전력 최적화
• DevFreq를 이용한 종합적 전력관리
하드웨어
Hardware Component;
Homogeneous; Heterogeneous;
Multicore System; Dynamic
Voltage and Frequency; Devfreq
Framework; Component Power
Management, …
Mobile
Embedded
System
Lab.,
Yonsei
University
4

5. Researches

6. Hardware-­‐level
Power
Management
(1)
CPU
Display
Dynamic
Voltage
&
Frequency
Scaling
Brightness
Level
Control
Frequency
Scaling
RGB
Level
Conversion
GPU
Network
Sensors
Adapave
Clock
Rate
Control
Opportunisac
Sensing
Scheduling
Mobile
Embedded
System
Lab.,
Yonsei
University
6

7. Hardware-­‐level
Power
Management
(2)
• DVFS
(Dynamic
Voltage
and
Frequency
Scaling)
– Voltage
and
frequency
scaling
are
oden
used
together
to
save
power
in
mobile
devices
including
cell
phones.
• DVFS
in
Android/Linux
(Power
Governor)
Ondemand
Features
ü DVFS only
Performance
Powersave
Hotplug
PegasusQ
ü Set the CPU sta
tically to the hig
hest frequency
ü Set the CPU sta
tically to the low
est frequency
ü Dual-core
ü Based on Onde
mand
ü Multi-core
ü Based on Onde
mand
Frequency C
ontrol
ü Utilization
ü CPU Frequency
―
―
ü Utilization
ü CPU Frequency
ü Utilization
ü CPU Frequency
Multi-core Ma
nagement
―
―
―
ü Average CPU U
tilization
ü CPU Frequency
ü # of Processes
Mobile
Embedded
System
Lab.,
Yonsei
University
7

8. Hardware-­‐level
Power
Management
(3)
• Is
DVFS
really
(or
always)
energy-­‐efficient?
– “DVFS
scheme
reduces
power
consumpaon,
which
can
lead
to
significant
reducaon
in
the
energy
required
for
a
computaaon,
paracularly
for
memory-­‐
bound
(I/O-­‐bound)
workloads”
*
CPU-­‐bound
2
8
5
10
10
8
900
J
2
1080
J
5
900
J
2
16
10
900
J
I/O-­‐bound
Inefficient
10
5
900
J
ame
CPU
jobs
4
18
15
8
720
J
12
ame
Efficient
I/O
(memory)
jobs
Performance
loss
in
every
case
*
Le
Sueur,
and
Heiser,
G.,
“Dynamic
Voltage
and
Frequency
Scaling:
the
Laws
of
Diminishing
Returns,”
HotPower’10
Mobile
Embedded
System
Lab.,
Yonsei
University
8

9. Hardware-­‐level
Power
Management
(4)
• OLED
– OLED
display
power
model
is
a
linear
funcaon
of
linear
RGB
intensity
levels.
– Different
OLED
displays
have
different
power
models
• Chameleon*
25%ê
34%ê
72%ê
66%ê
*
M.
Dong
and
L.
Zhong,
“Chameleon:
a
color-­‐adapave
web
browser
for
mobile
OLED
displays”,
MobiSys
2011.
Mobile
Embedded
System
Lab.,
Yonsei
University
9

10. Hardware-­‐level
Power
Management
(5)
• LCD
(and
OLED)
– Reducing
brightness
level
without
UX-­‐loss
About
21%
reducaon
of
power
consumpaon
with
almost
same
UX
as
original
image
B.
Anand
et
al.,
“Adapave
display
power
management
for
mobile
games”,
MobiSys
2011.
Mobile
Embedded
System
Lab.,
Yonsei
University
10

11. Energy
Bugs/Hogs
(1)
Energy
Bugs
• Some
running
instance
of
the
app
drain
the
baWery
significantly
faster
than
other
instance
of
the
same
app
• Cause
-­‐
Coding
error
-­‐
Rare
configuraaon
-­‐
Unusual
user
behavior
• Remedy
-­‐
Restart
the
energy
bug
app
-­‐
Kill
the
energy
bug
app
Energy
Hogs
• The
app
drains
the
baWery
significantly
faster
than
the
average
app
• Cause
-­‐
Coding
error
-­‐
Using
large
amounts
of
energy
to
serve
its
funcaon
(ex,
device
resources..)
• Remedy
-­‐
Kill
the
energy
hog
app
A.
J.
Oliner,
A.
Iyer,
E.
Lagerspetz,
S.
Tarkoma
and
I.
Stoica,
“Collaboraave
Energy
Debugging
for
Mobile
Devices,”
in
Proc.
of
the
8th
USENIX
conference
on
Hot
Topics
in
System
Dependability,
Berkeley,
CA,
USA,
October
2012.
Mobile
Embedded
System
Lab.,
Yonsei
University
11

12. Energy
Bugs/Hogs
(2)
• Diverse
causes
of
Energy
Bugs
– An
error
in
the
system,
either
applicaaon,
OS,
hardware,
firmware
or
external
that
causes
an
unexpected
amount
of
high
energy
consumpaon
by
the
system
as
a
whole
A.
Pathak,
Y.
C.
Hu
and
M.
Zhang,
“Bootstrapping
energy
debugging
on
smartphones:
a
first
look
at
energy
bugs
in
mobile
devices,”
in
Proc.
of
the
10th
ACM
Workshop
on
Hot
Topics
in
Networks,
Cambridge,
MA,
USA,
November
2012.
Mobile
Embedded
System
Lab.,
Yonsei
University
12

13. Energy
Bugs/Hogs
(3)
• Managing
Energy
Bugs/Hogs
– Diagnose:
compare
normal
baWery
drain
and
abnormal
baWery
drain
– Suggest
appropriate
repair
soluaons
based
on
the
diagnosis
results
Diagnosis
Engine
Informa8on
Collector
Anomaly
Detecaon
User
Changes
Resource
Usage
Suspicious
Resource
Usage
Repair
Advisor
Suspicious
Events
eDoctor*
:
Phase
Analysis
Delete
Apps
Revert
Apps
Terminate
Apps
Revert
Configs
Data
Analyzer
Phase
Idenaficaaon
Per-­‐applicaaon
usage
paWerns
System
wide
usage
paWerns
Configuraaon
paWerns
Sampling
during
discharging
Compare
reference
and
subject
Carat
:
Comparison
Analysis
*
X.
Ma,
P.
Huang,
X.
Jin,
P.
Wang,
S.
Park,
D.
Shen,
Y.
Zhou,
L.
K.
Saul,
and
G.
M.
Voelker,
“eDoctor
:
Automaacally
Diagnosing
Abnormal
BaWery
Drain
Issues
on
Smartphones,”
in
Proc.
of
the
10th
USENIX
Symposium
on
NSDI’
13,
Berkeley,
CA,
USA,
April
2013.
Mobile
Embedded
System
Lab.,
Yonsei
University
13

14. Energy
Bugs/Hogs
(4)
• Default
power
management
policy
for
mobile
device
(new
paradigm)
– OS
uses
aggressive
sleeping
policies
– Every
component,
including
the
CPU,
stays
off
or
in
an
idle
state,
unless
the
app
explicitly
instructs
the
OS
to
keep
it
on!
• “No-­‐sleep”
Energy
Management
– Aggressive
sleeping
may
severely
impacts
smartphone
apps
– Power
encumbered
programming
:
Androids
“Wakelock”
API
New
Energy
Bug*
à
“No-­‐sleep”
bug
:
70%
(applica8on)
*
Pathak,
Abhinav,
et
al,
“What
is
keeping
my
phone
awake?:
characterizing
and
detecang
no-­‐sleep
energy
bugs
in
smartphone
apps,”
in
Proc.
of
the
10th
internaRonal
conference
on
Mobile
systems,
applicaRons,
and
services
(MobiSys
2012),
ACM,
2012.
Mobile
Embedded
System
Lab.,
Yonsei
University
14

15. Energy
Bugs/Hogs
(5)
• WakeScope
(mobed.yonsei.ac.kr/wakescope)
– A
runame
WakeLock
anomaly
management
scheme
for
Android
plauorm
WakeLock
Behavior
Tracker
Applica8on
WakeLock
Anomaly
Detector
Android
System
PARTIAL
PARTIAL
SCREEN
PARTIAL
SCREEN
Android
Framework
WakeLock
behavior
tracking
Applicaaon
FULL
FULL
Binder
PARTIAL
SCREEN
FULL
…
WakeScope
Applica8on
Applicaaon
&
Android
system
stop
state
checking
CPU
Usage
Process
Running
State
Android
System
SCREEN
FULL
WakeLock
release
checking
PowerManagerService
Android
Power
Management
WakeLock
behavior
tracking
Android
System
PARTIAL
“PowerManagerService”
PARTIAL
…
WakeLock
Anomaly
checking
PARTIAL
Screen
state
SCREEN
FULL
Light
off
ame
“…..”
PARTIAL
Linux
Power
Management
WakeLock
Anomaly
Mobile
Embedded
System
Lab.,
Yonsei
University
Handling
of
WakeLock
Anomaly
Applica8on
Android
System
Kill
Applicaaon
Reboot
Smartphone
15

16. Energy-­‐aware
UX
(1)
• Beder
energy-­‐related
understandings
à
energy-­‐efficient
behavior
1.6
Donut
Android
BaWery
Informaaon
4.1.1
Jellybean
TCBI*
• Task-­‐centered
Badery
Interface*
– Support
users’
mental
models
on
fully
understanding
what
is
happening
on
their
devices
*K.
N.
Truong,
et
al.
"The
Design
and
Evaluaaon
of
a
Task-­‐Centered
BaWery
Interface,“
UbiComp
2010.
Mobile
Embedded
System
Lab.,
Yonsei
University
16

17. Energy-­‐aware
UX
(2)
• HCI-­‐based
Display
Control
– Reduce
display
power
by
dimming
the
parts
of
an
applicaaon
or
game
that
are
of
low
interest
Brighten
user-­‐
interest
area
Dim
less
important
area
Wee,
Tan
Kiat,
et
al.
"DEMO
of
Focus:
A
Usable
&
Effecave
Approach
to
OLED
Display
Power
Management,“
HotMobile
2013.
Mobile
Embedded
System
Lab.,
Yonsei
University
17

18. Energy-­‐aware
UX
(3)
• Ac8ve
User
Involvement
– User
is
a
main
actor
for
energy
management
M.
Marans
and
R.
Fonseca
"Applicaaon
Modes:
A
Narrow
Interface
for
End-­‐User
Power
Management
in
Mobile
Devices,“
HotMobile
2013.
Mobile
Embedded
System
Lab.,
Yonsei
University
18

19. Energy-­‐aware
UX
(4)
• Badery
Virtualiza8on
– Virtualizaaon
of
the
baWery
resource
across
applicaaon
classes
Virtual
BaWery
1
Physical
Badery
Virtual
BaWery
2
Virtual
BaWery
3
App
Class
1
App
Class
2
App
Class
3
N.
Zhang
et
al.
“PowerVisor:
a
baWery
virtualizaaon
scheme
for
smartphones,“
MCS
2012.
Mobile
Embedded
System
Lab.,
Yonsei
University
19

20. Energy-­‐aware
UX
(5)
• Context-­‐based
Badery
Management:
BaderyGuru
(Qualcomm)
– Extends
baWery
performance
and
improves
overall
user
experience
by
intelligently
making
changes
that
opamize
device
funcaonality
Preferred
Applicaaons
Preferred/Available
WiFi
regions
Automaac
learning
• Manage
applicaaon’s
update
points
• Control
WiFi
on/off
Badery
Saving
Mobile
Embedded
System
Lab.,
Yonsei
University
20

21. Es8ma8on

22. Power
Modeling
&
Energy
Es8ma8on
(1)
• Why
applica8on/component
energy
informa8on
is
valuable?
App.
Developer
System
Developer
Applica8on/Hardware
Energy
Metering
End
User
Mobile
Embedded
System
Lab.,
Yonsei
University
22

23. Power
Modeling
&
Energy
Es8ma8on
(2)
Ualizaaon
Challenges:
how
to
estimate
application’s
energy
consumption?
CPU
Display
Acave
cores
…
​ 𝑃↓𝐶𝑃𝑈 =​ 𝛽↓𝑓 ​⋅ 𝑈↓𝐶𝑃𝑈 +​ 𝛽↓𝑖𝑑𝑙𝑒 ⋅(1−​ 𝑈↓𝐶𝑃𝑈 )
GPU
​ 𝑃↓𝑂𝐿𝐸𝐷 = 𝑓(𝑅, 𝐺, 𝐵)
⋅
​ 𝑃↓𝐺𝑃𝑈 =​↓𝑅 ​ 𝑅⋅↓𝑖 ↓𝐺𝑃𝑈 +​ ↓𝑖 +WiFi 𝐵↓𝑖 )
(​ 𝛽 𝛽↓𝑓 ​ 𝑈 +​ 𝛽↓𝐺 ​ 𝐺 𝛽↓𝑏𝑎𝑠𝑒
​ 𝛽
↓𝐵 ​
​ 𝑃↓3 𝐺 =​ 𝛽↓3 𝐺 , 3 𝐺={ 𝑈𝑀𝑇𝑆, 𝐻𝑆𝑈𝑃𝐴, 𝐻𝑆𝑃𝐴𝑃}
GPS
​ 𝑃↓𝐺2 𝐷𝑋 =​ 𝛽↓𝐺2 𝐷𝑋 ⋅​ 𝑃 𝑖𝑥𝑒𝑙𝑠↓𝐺2 𝐷𝑋 +​ 𝛽↓𝑏𝑎𝑠𝑒
Cell
Frequency
Colors/
Brightness
Display
occupancy
ame
Connected
ame
3G/LTE
High/low
Power
state
TRX
packets
RRC
state
Mobile
Embedded
System
Lab.,
Yonsei
University
23

24. Device
Power
Modeling
(1)
Base Power
Component Power
GPU
DISPLAY
CPU
​ 𝑃↓𝑡↑𝑡𝑜𝑡𝑎𝑙 =​ 𝑃↓𝑏𝑎𝑠𝑒
+∑𝑖=1↑𝑛▒​ 𝑃↓𝑡↑𝑖 +​ 𝜖↓𝑡
Total Power
Error, Leakage
Maximum power consumption of
𝑖
component
​ 𝑃↓𝑡↑𝑖 =​ 𝛽↓𝑖 ⋅​ 𝑥↓𝑖↑𝑡
Component behavior
and usage
Mobile
Embedded
System
Lab.,
Yonsei
University
24

25. Device
Power
Modeling
(2)
• Issues
– Component-­‐specific
ualizaaon
metric
• uame/same,
cores,
frequency,
color/brightness,
packet,
tail
state,
temp,
…
– Base
power
esamaaon
• System
base
power,
component
base
power
– Isolated
control
of
hardware
component
• Isolaang
CPU
influence
from
component
power
monitoring
Base power ?
Colors/Brightness
TRX
packets
Ualizaaon
Power
of
component
Acave
cores
How to fix the
𝐶
​ 𝑷↓𝒄𝒑𝒖 ?
Frequency
Mobile
Embedded
System
Lab.,
Yonsei
University
25

26. Device
Power
Model:
Example
Mobile
Embedded
System
Lab.,
Yonsei
University
26

27. Power
Analysis

28. Conven8onal
Power
Measurement
acave
core?
base?
Job
2
triggering?
Job
3
Job
3
temperature?
Job
2
governor?
Job
1
frequency?
Job
1
External
Power
Monitor
Mobile
Embedded
System
Lab.,
Yonsei
University
28

29. Nonintrusive
Power
Measurement
(1)
…
CPU Display Wi-­‐Fi Cellular GPS
Fuel-gauge IC
H/W
Components
Battery
DevScope
Component
Controller
Timing
Controller
Fuel-­‐gauge
IC
Event
Monitor
Component Power Model
Power
Model
Generator
• Concepts
– Use
built-­‐in
fuel-­‐gauge
IC
– Assume
specific
H/W
power
model
– Online
and
non-­‐intrusive
power
modeling
• Power
modeling
process
– Component-­‐specific
training
set
generaaon
• Workload,
Control
scenario
– Probe
OS,
H/W
component
– Monitor
fuel-­‐gauge
IC
Mobile
Embedded
System
Lab.,
Yonsei
University
29

30. Nonintrusive
Power
Measurement
(2)
• Badery
Monitoring
Unit
(BMU,
BMIC,
Fuel-­‐gauge
IC,
SBS,
…)
– Internal
measurement
device
for
supply
voltage,
baWery
capacity,
discharge
current,
temperature,
...
• BMIC
types
Type
Current + voltage
Voltage only
IC
Maxim DS2760, Maxim DS278X,
TI UCC3926, …
Ti BQ27X00, WM97XX, …
Model
Sony-Ericsson Xperia Series,
HTC NexusOne/Desire Series,
Samsung Galaxy S3/S4(?)
Samsung Galaxy Series,
LG Optimus Series(?),
…
• Example:
Maxim
DS2784
(HTC
Nexus
One)
– Measuring
voltage,
current,
and
temperature
– Current
sensing
• Resoluaon:
104uA
• Sensing
range:
-­‐3430mA
~
3430mA
(±1%)
• Sampling
rate:
18.6KHz
/
Update
rate:
0.28Hz
Mobile
Embedded
System
Lab.,
Yonsei
University
BaWery
Monitoring
Unit
(Maxim
DS2784)
30

31. Nonintrusive
Power
Measurement
(3)
DevScope
Coefficient Values
Workload
Genera8on
𝑝=​ 𝛽↓𝑓𝑟𝑒𝑞 × 𝑢
-­‐
WiFi:
𝑝=​ 𝛽↓𝑇𝑋 × 𝑝𝑝𝑠+​ 𝛽↓𝑖𝑑𝑙𝑒
Ini8aliza8on
Component
P ​ 𝛽↓𝑝𝑜𝑤𝑒𝑟_ 𝑠𝑡𝑎𝑡𝑒
-­‐
3G/GPS:
𝑝=ower
Models
-­‐
LCD:
𝑝=​ 𝛽↓𝑏𝑟𝑖𝑔ℎ𝑡𝑛𝑒𝑠𝑠
-­‐
CPU:
Workload
Tuning
&
Probing
CPU
LCD
WiFi
3G
GPS
245
368
238
268
0
998
854
247
519
354
Power
behavior
analysis
Android
-­‐
CPU
frequencies
-­‐
BMU
types
-­‐
Hardware
component
types
-­‐
Network
se|ngs
Reporang
Rate
Measurement
Data
BMU
Ac8vity
System
Configura8on
-­‐
CPU
control
-­‐
3G/LTE/WiFi
networking
-­‐
Display
control
-­‐
GPS
control
Android
API
BMU
Report
Hardware
CPU
BMU
Mobile
Embedded
System
Lab.,
Yonsei
University
LCD
WiFi
Cellular
GPS
Hardware
Components
31

32. U8liza8on

33. Applica8on
Energy
Es8ma8on
(1)
Challenges:
how
to
estimate
application’s
energy
consumption?
• Conven8onal
methods
​ 𝐸↑𝐴𝑝𝑝 =∑𝑖=0↑# 𝑜𝑓𝐶𝑜𝑚𝑝▒(​ 𝛽↓𝑖 ×​ 𝑥↓𝑖↑𝐴𝑝𝑝 )×​ 𝑑↓𝑖↑𝐴𝑝𝑝
– HPC
(hardware
performance
counter)
– Linux
“procfs/sysfs”
​ 𝛽↓𝑖↑
​ 𝑥↓𝑖↑𝐴𝑝𝑝
Power
coefficient
value
Utilization
– Android
“BaYeryStats”
– Basically
“polling”
• Problems
– Dependency
on
processor
– Update
rate
problem
– Granularity
problem
(per-­‐process
info)
​ 𝑑↓𝑖↑𝐴𝑝𝑝
Activated
duration
Hardware
component
usage
Limitations
Accuracy/Granularity/Real-­‐time
Mobile
Embedded
System
Lab.,
Yonsei
University
33

34. Applica8on
Energy
Es8ma8on
(2)
• How
to
detect
hardware
component
opera8on?
– Event-­‐driven
kernel
acavity
monitoring
Event
of
component
opera8on
Dura8on/U8liza8on
of
opera8on
t
Mobile
Embedded
System
Lab.,
Yonsei
University
34

35. Applica8on
Energy
Es8ma8on
(3)
Android API call
Application
binder
...
process 2
c/c++
libraries
process n
Kernel Activity Monitoring
Event Detector
Kernel
Binder IPC data
Android
process 1
1
𝑖
binder
driver
Request to use H/W
by system call
Energy
Consumption
H/W
drivers
binder_ioctl(), ioctl(), socket(), read(), write(), ...
2
Hardware Component Usage
Analyzer
…
CPU
Display
Wi-Fi
Cellular
GPS
3
Application Energy Estimator
Component Power Models
Fuel-gauge
IC
Estimation
Result
External
Devices
Device
power
model
Mobile
Embedded
System
Lab.,
Yonsei
University
35

36. Component
Usage
Monitoring
(1)
“Kprobes”
Kernel
path
Instrumentaaon
rouane
• System
calls
• Kernel
funcaons
• Binder
calls
Hooking
Point
0x81808c144
Probe
Handler
Hardware
Component
Usage
Analyzer
INT03:Break

37.
Event
detector
•
•
•
•
•
•
•
binder_ioctl()
binder_transacRon()
cpufreq_cpu_put()
sched_switch()
dev_queue_xmit()
neRf_rx()
…
•
•
•
•
•
Return
to
original
path

38.
•
CPU
frequency/ualizaaon
WiFi
transmission
packet
r
ate
LCD
display
duraaon
GPS
acavated
duraaon
3G
network
connecaon
ty
pe
…
End
Mobile
Embedded
System
Lab.,
Yonsei
University
36

39. Component
Usage
Monitoring
(2)
AppScope
Kernel function calls
Kernel
Process 1
Packet
transmitting
Frequency
changing
Process
switching
Event Detector
Process 2
Process 3
dev_queue_xmit()/netif_rx()
H/W Component
Usage Analyzer
Time(s)
cpufreq_cpu_put()
998.4 MHz
sched_switch()
806.4 MHz
614.4 MHz 245.0 MHz
Timing
synchronization
Time (tick)
Utilization (%)
Time (tick)
Mobile
Embedded
System
Lab.,
Yonsei
University
37

40. Component
Usage
Monitoring
(3)
• Display
(OLED)
– (R,
G,
B)
and
brightness
𝑃= 𝐵𝑟× 𝑓(𝑅, 𝐺, 𝐵)
×(​ 𝛽↓𝑅 ​ 𝑅↓𝑖 +​ 𝛽↓𝐺 ​
𝐺↓𝑖 +​ 𝛽– Accurate
OLED
energy
↓𝐵 ​ 𝐵↓𝑖 )
esamaaon
by
Framebuffer
Analysis
– Event-­‐based
acavity
transiaon
monitoring
• Applicaaon-­‐specific
display
power
esamaaon
Mobile
Embedded
System
Lab.,
Yonsei
University
38

41. Applica8on
Energy
Es8ma8on:
Overall
Framework
Device
Power
Modeling
(Training)
Kernel
source
Energy
Es8ma8on
Behavior
analysis
Power
Analysis
Formulaaon
System
Evalua8on
External
Power
Monitor
Mobile
Embedded
System
Lab.,
Yonsei
University
Error
Analysis
39

42. AppScope

43. The
AppScope
Project
(1)
App.
Developer
System
Sooware
Developer
Energy
Bug
Report
/
Energy
Consump8on
Sta8s8cs
AppScope
AppScope
Library
Single
Core
Mul8
Core
LCD
OLED USPA
LTE
CPU
Display
Wi-­‐Fi
Cell
GPS
…
AppScopeViewer
AppScopeViewer
I/F
Applica8ons
End
User
Simple
On/Off
States
Component
X
Power
Models
(DevScope/Vendor)
C.
Yoon,
D.
Kim,
W.
Jung,
C.
Kang,
H.
Cha,
'AppScope:
Applicaaon
Energy
Metering
Framework
for
Android
Smartphone
using
Kernel
Acavity
Monitoring,
USENIX
Annu
al
Technical
Conference
(USENIX
ATC'12),
2012
W.
Jung,
C.
Kang,
C.
Yoon,
D.
Kim,
H.
Cha,
DevScope:
A
Nonintrusive
and
Online
Power
Analysis
Tool
for
Smartphone
Hardware
Components,
InternaRonal
Confere
nce
on
Hardware/So_ware
Codesign
and
System
Synthesis
(CODES+ISSS'12),
2012
Mobile
Embedded
System
Lab.,
Yonsei
University
41

44. Demo
(AppScope/AppScopeViewer)
hdp://mobed.yonsei.ac.kr/appscope
Mobile
Embedded
System
Lab.,
Yonsei
University
42

45. Current
Status
of
AppScope
1.9GHz
Quad.
Media
processor
Mali-­‐400
GPU
Adreno
320
GPU
1.4GHz
Quad.
2D
graphics
Snapdragon
600
Exynos-­‐4412
3G
SKT
HSDPA
VEGA
IRON
Galaxy
S3
GPS
WiFi
Nexus
One
LCD
Display
Camera
Snapdragon
QSD8250(1GHz)
Tail
Power
AMOLED
Display
3G
HSPA+
Galaxy
S4
Exynos-­‐5410
4G
LTE
1.6GHz
Octa.
big.LITTLE
Media
processor(MFC) SGX
544
GPU
Mobile
Embedded
System
Lab.,
Yonsei
University
43

46. AppScope
Remaining
Challenges
Sensors
Touch screen
Bluetooth
NFC
…
More
components
Thermal
power
modeling
3G
HSPA+
4G
LTE
WiFi
Autonomous
power
modeling
Signal-­‐strength-­‐aware
modeling
Mobile
Embedded
System
Lab.,
Yonsei
University
44

47. AppScope
Capabili8es
Poten8als
Energy-­‐
aware
UX
• Interacave
UX
• Lifeame
predicaon
• App.
running
state
analysis
Program
bugs
Wakelock
Handover
Signal
strength
Energy
• User
behavior
monitoring
Bugs/Hogs • Anomaly
detecaon
• Debugging
frameworks
Detec8on
30%
Energy
Mgmt.
Power
Mgmt.
• Energy
management
API
• BaWery
virtualizaaon
• Remain
capacity
predicaon
• CPU
power
governor
• DEVFREQ
(OPP)
• Mula-­‐core
task
scheduling
Multicore
30%
100%
40%
GPU
big.LITTLE MFC
Task scheduling
Workload • Workload
characterizaaon
Analysis • Load
monitoring
• Detecang
workload
unbalance
Mobile
Embedded
System
Lab.,
Yonsei
University
45

48. Our
Researches

49. Work
@
Yonsei
Network
Env.
User
Interac8on
Network
Serv.
Badery
State
Wi-Fi
Cellular
AppScope
GPS
…
Display
Calling
Serv.
…
…
CPU
UserScope
WakeScope
• Component
usage
monitoring
• Applicaaon
energy
esamaaon
• System
energy
esamaaon
• Energy
usage
profiling
• Energy
habit
monitoring
• Energy
bugs/hogs
detecaon
• Component
power
modeling
• Mula-­‐core
CPU,
GPU,
OLED,
…
• Online
power
modeling
•
•
•
•
• EMSOFT2013,
…
• USENIX
ATC’12
• CODES+ISSS2012,
DATE2013
Fuel-gauge IC
H/W
Components
…
CPU Display Wi-­‐Fi Cellular GPS
p
Battery
DevScope
Power
Management
Mula-­‐core
CPU
governor
DEVFREQ
frameworks
Personalized
energy
mgnt.
Adapave
duty
cycling
• SenSys’11,
...
Active State
Hidden State1
Hidden State2
Active State
Hidden
State 1
Hidden
State 2
Base
3R
4R
Mobile
Embedded
System
Lab.,
Yonsei
University
47

50. FIN
hWp://mobed.yonsei.ac.kr/