144 deview-2013-smartphone pm

연세대학교 컴퓨터과학과 모바일 임베디드 시스템 연구실

윤찬민 ([email protected])

DEVIEW 2013 2013.10.14

Energy Management for Mobile Devices: Power Es8ma8on Technique for Modern Smartphones

Mobile Pla@orms

Mobile Embedded System Lab., Yonsei University 2

Feature Phone Galaxy S Galaxy S2 Galaxy S3

Cellular Bluetooth WiFi NFC

1GHz Single core GSM/HSDPA

480x800 Display, 4.0 inch 1500 mAh baWery

1.2GHz Dual core GSM/HSDPA


1.4GHz Quad core GSM/HSDPA/LTE


Camera Mic.

Accel Compass

Light GPS

Proximity

Thermometer Gyroscope Barometer

FM Radio

GSM

240x320 Display

Galaxy S4

1.6GHz Octa core GSM/WCDMA/LTE

1080x1920 Display(Full HD), 5.0 inch 2600 mAh baWery

IrDA

Gesture

Energy Management for Mobile Devices


𝛼

Energy Management Techniques


•  단말/응용 가용시간 예측 •  사용자 요구 및 컨텍스트 반영 •  사용자중심 Energy-aware UX

•  실시간 응용프로그램 에너지 bug/hog 감지

•  응용프로그램의 에너지 특성에 따른 에너지 bug 및 hog 원인 분석 및 리포팅 시스템

Battery Lifetime; User Interaction; Requirement; User Experience; Personalization; Quality of Service; User Context; Spatiotemporal Context, …

Application; Energy Anomaly; Energy Bug; Energy Hog; Energy Leakage; Wakelock; Non-Sleep; Anomaly Detection; Debugging;

Anomaly Reporting, …

사용자 응용 프로그램

•  응용프로그램의 전력 소모 특성 정보 수집 관리 기술

•  가상 배터리 관리 기법 •  Energy-aware OS

•  하드웨어 전력프로파일링 및 모델링 •  하드웨어 컴포넌트 전력 최적화 •  DevFreq를 이용한 종합적 전력관리

Energy Usage; Application Energy Estimation; Process Energy Estimation; Virtualization; Battery Segmentation; Resource Management, …

하드웨어 시스템 소프트웨어

Hardware Component; Homogeneous; Heterogeneous;

Multicore System; Dynamic Voltage and Frequency; Devfreq Framework; Component Power

Management, …

Researches

Hardware-‐level Power Management (1)


Frequency Scaling GPU

Dynamic Voltage & Frequency Scaling

CPU

Brightness Level Control Display

Opportunisac Sensing Scheduling Sensors

Adapave Clock Rate Control Network

RGB Level Conversion


•  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 Performance Powersave Hotplug PegasusQ

Features ü  DVFS only ü  Set the CPU sta

tically to the highest frequency

ü  Set the CPU statically to the lowest frequency

ü  Dual-core ü  Based on Onde

mand

ü  Multi-core ü  Based on Onde

mand

Frequency Control

ü  Utilization ü  CPU Frequency ― ― ü  Utilization

ü  CPU Frequency ü  Utilization ü  CPU Frequency

Multi-core Management ― ― ― ü  Average CPU U

tilization ü  CPU Frequency ü  # of Processes


•  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” *

* Le Sueur, and Heiser, G., “Dynamic Voltage and Frequency Scaling: the Laws of Diminishing Returns,” HotPower’10

CPU-‐bound

I/O-‐bound

8 2

5 5

2 8 ame

10

16 2

10 5

18

15

CPU jobs I/O (memory) jobs

4 8 12 ame

900 J

1080 J

900 J

900 J

10

10

900 J

720 J

Inefficient

Efficient

Performance loss in every case



•  OLED –  OLED display power model is a linear funcaon of linear RGB intensity levels. –  Different OLED displays have different power models

•  Chameleon*

* M. Dong and L. Zhong, “Chameleon: a color-‐adapave web browser for mobile OLED displays”, MobiSys 2011.

25%ê 34%ê 72%ê 66%ê



B. Anand et al., “Adapave display power management for mobile games”, MobiSys 2011.

About 21% reducaon of power consumpaon with almost same UX as original image

•  LCD (and OLED) –  Reducing brightness level without UX-‐loss


Energy Bugs/Hogs (1)

•  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 Bugs

•  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

Energy Hogs

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.



•  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.



•  Managing Energy Bugs/Hogs –  Diagnose: compare normal baWery drain and abnormal baWery drain –  Suggest appropriate repair soluaons based on the diagnosis results

Informa8on Collector

Resource Usage

User Changes

Data Analyzer Phase

Idenaficaaon Per-‐applicaaon usage paWerns

System wide usage paWerns

Configuraaon paWerns

Diagnosis Engine Anomaly Detecaon

Suspicious Resource Usage

Suspicious Events

Repair Advisor

Delete Apps

Revert Apps

Terminate Apps

Revert Configs

* 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.


Sampling during discharging

Compare reference and subject

eDoctor* : Phase Analysis

Carat : Comparison Analysis


•  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.




•  WakeScope (mobed.yonsei.ac.kr/wakescope) –  A runame WakeLock anomaly management scheme for Android plauorm

SCREEN FULL PARTIAL PARTIAL …

WakeLock Anomaly

WakeLock Anomaly Detector

Process Running State

CPU Usage

Applicaaon & Android system stop state checking

WakeLock release checking

WakeLock Anomaly checking

Screen state Light off ame

SCREEN FULL PARTIAL

WakeScope Applica8on

Handling of WakeLock Anomaly

Kill Applicaaon

Reboot Smartphone

Applica8on Android System

Linux Power Management

“PowerManagerService”

PARTIAL “…..”

PARTIAL …

Applica8on Android System

WakeLock behavior tracking

PARTIAL SCREEN FULL Applicaaon Android System

SCREEN FULL

WakeLock behavior tracking Android System

PARTIAL

WakeLock Behavior Tracker

Binder

Android Framework

PARTIAL SCREEN FULL

PowerManagerService

Android Power Management

Energy-‐aware UX (1)

•  Beder energy-‐related understandings à energy-‐efficient behavior

•  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.


TCBI* Android BaWery Informaaon 1.6 Donut 4.1.1 Jellybean


•  HCI-‐based Display Control –  Reduce display power by dimming the parts of an applicaaon or game that are of

low interest

Wee, Tan Kiat, et al. "DEMO of Focus: A Usable & Effecave Approach to OLED Display Power Management,“ HotMobile 2013.

Brighten user-‐interest area

Dim less important area



•  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.



•  Badery Virtualiza8on –  Virtualizaaon of the baWery resource across applicaaon classes

N. Zhang et al. “PowerVisor: a baWery virtualizaaon scheme for smartphones,“ MCS 2012.

Physical Badery

Virtual BaWery 1

Virtual BaWery 2

Virtual BaWery 3

App Class 1

App Class 2

App Class 3


Badery Saving


•  Context-‐based Badery Management: BaderyGuru (Qualcomm) –  Extends baWery performance and improves overall user experience by

intelligently making changes that opamize device funcaonality


•  Manage applicaaon’s update points •  Control WiFi on/off

Preferred Applicaaons Preferred/Available WiFi regions

Automaac learning

Es8ma8on

Power Modeling & Energy Es8ma8on (1)

•  Why applica8on/component energy informa8on is valuable?

Applica8on/Hardware Energy Metering

App. Developer

System Developer

End User Mobile Embedded System Lab., Yonsei University 22

Power Modeling & Energy Es8ma8on (2)

Challenges: how to estimate application’s energy consumption?


CPU

GPS

Cell

WiFi

Display

GPU 𝑃↓𝐶𝑃𝑈 = 𝛽↓𝑓 ⋅𝑈↓𝐶𝑃𝑈 + 𝛽↓𝑖𝑑𝑙𝑒 ⋅(1− 𝑈↓𝐶𝑃𝑈 ) 𝑃↓𝑂𝐿𝐸𝐷 =𝑓(𝑅,𝐺,𝐵) ⋅

(𝛽↓𝑅 𝑅↓𝑖 + 𝛽↓𝐺 𝐺↓𝑖 + 𝛽↓𝐵 𝐵↓𝑖 ) 𝑃↓𝐺𝑃𝑈 = 𝛽↓𝑓 ⋅ 𝑈↓𝐺𝑃𝑈 + 𝛽↓𝑏𝑎𝑠𝑒  𝑃↓3𝐺 = 𝛽↓3𝐺 , 3𝐺={𝑈𝑀𝑇𝑆,𝐻𝑆𝑈𝑃𝐴,𝐻𝑆𝑃𝐴𝑃}

𝑃↓𝐺2𝐷𝑋 = 𝛽↓𝐺2𝐷𝑋 ⋅ 𝑃𝑖𝑥𝑒𝑙𝑠↓𝐺2𝐷𝑋 + 𝛽↓𝑏𝑎𝑠𝑒  …

Acave cores Frequency

Ualiza

aon

Colors/ Brightness

Display occupancy ame

3G/LTE

RRC state

Connected ame

High/low Power state

TRX packets

Device Power Modeling (1)


CPU

DISPLAY

GPU

𝑃↓𝑡↑𝑖 = 𝛽↓𝑖 ⋅ 𝑥↓𝑖↑𝑡 

Component behavior and usage

𝑃↓𝑡↑𝑡𝑜𝑡𝑎𝑙 = 𝑃↓𝑏𝑎𝑠𝑒 +∑𝑖=1↑𝑛▒𝑃↓𝑡↑𝑖  + 𝜖↓𝑡 

Total Power

Base Power

Error, Leakage

Component Power

Maximum power consumption of

component 𝑖

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 ?

How to fix the 𝑷↓𝒄𝒑𝒖  ?

Acave cores Frequency

Ualiza

aon

TRX packets

Colors/Brightness

Power of component 𝐶

Device Power Model: Example


Power Analysis

Conven8onal Power Measurement


External Power Monitor

Job 1

Job 2

Job 3

triggering?

frequency?

acave core? temperature?

base?

governor?

Job 1

Job 2

Job 3

Nonintrusive Power Measurement (1)

Fuel-gauge IC

DevScope

Component Controller

TimingController

Fuel-‐gauge IC Event

Monitor

Power Model

Generator

H/W Components

…CPU Display Wi-‐Fi Cellular GPS

Battery

Component Power Model •  Power modeling process –  Component-‐specific training set

generaaon •  Workload, Control scenario

–  Probe OS, H/W component –  Monitor fuel-‐gauge IC

•  Concepts –  Use built-‐in fuel-‐gauge IC –  Assume specific H/W power

model –  Online and non-‐intrusive power

modeling



•  Badery Monitoring Unit (BMU, BMIC, Fuel-‐gauge IC, SBS, …) –  Internal measurement device for supply voltage, baWery capacity, discharge

current, temperature, ...

•  BMIC types

•  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

BaWery Monitoring Unit (Maxim DS2784)

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(?), …



Hardware

Android

DevScope

System Configura8on

-‐ CPU frequencies -‐ BMU types -‐ Hardware component types -‐ Network se|ngs

Android API

-‐ CPU control -‐ 3G/LTE/WiFi networking -‐ Display control -‐ GPS control

Component Power Models

-‐ CPU: 𝑝= 𝛽↓𝑓𝑟𝑒𝑞 ×𝑢

-‐ WiFi: 𝑝= 𝛽↓𝑇𝑋 ×𝑝𝑝𝑠+ 𝛽↓𝑖𝑑𝑙𝑒  -‐ 3G/GPS: 𝑝= 𝛽↓𝑝𝑜𝑤𝑒𝑟_𝑠𝑡𝑎𝑡𝑒  -‐ LCD: 𝑝= 𝛽↓𝑏𝑟𝑖𝑔ℎ𝑡𝑛𝑒𝑠𝑠 

BMU Report

Measurement Data

Ini8aliza8on

Workload Genera8on

BMU Ac8vity

Reporang Rate

Workload Tuning

& Probing Power behavior analysis

Coefficient Values CPU LCD WiFi 3G GPS

245 368 238 268 0

998 854 247 519 354

Hardware Components CPU LCD WiFi Cellular GPS

BMU


U8liza8on

Hardware component usage

Applica8on Energy Es8ma8on (1)

𝐸↑𝐴𝑝𝑝 =∑𝑖=0↑#𝑜𝑓𝐶𝑜𝑚𝑝▒(𝛽↓𝑖 × 𝑥↓𝑖↑𝐴𝑝𝑝 )× 𝑑↓𝑖↑𝐴𝑝𝑝  

𝑥↓𝑖↑𝐴𝑝𝑝 

𝑑↓𝑖↑𝐴𝑝𝑝 

𝛽↓𝑖↑  Power coefficient value

Utilization

Activated duration Limitations Accuracy/Granularity/Real-‐time

•  Conven8onal methods –  HPC (hardware performance counter) –  Linux “procfs/sysfs” –  Android “BaYeryStats” –  Basically “polling”

•  Problems –  Dependency on processor –  Update rate problem –  Granularity problem (per-‐process info)


Challenges: how to estimate application’s energy consumption?


•  How to detect hardware component opera8on? –  Event-‐driven kernel acavity monitoring

t

Event of component opera8on

Dura8on/U8liza8on of opera8on



Estimation Result

Event DetectorApplication KernelAndroidprocess 1

process 2

process n

...

binder

c/c++ libraries

H/W drivers

binderdriver

Android API call

Binder IPC data

Request to use H/W by system call

Kernel Activity Monitoring

binder_ioctl(), ioctl(), socket(), read(), write(), ...

1

Hardware Component Usage Analyzer

2Application Energy Estimator

3

Energy Consumption

…CPU Display Wi-Fi Cellular GPS

Component Power Models

Fuel-gaugeIC

External Devices

Device power model

𝑖


Event detector

•  binder_ioctl() •  binder_transacRon() •  cpufreq_cpu_put() •  sched_switch() •  dev_queue_xmit() •  neRf_rx() •  …

Kernel path

Hooking Point

Probe Handler

0x81808c144

End

Instrumentaaon rouane

Return to original path��

Hardware Component Usage Analyzer

•  CPU frequency/ualizaaon •  WiFi transmission packet r

ate •  LCD display duraaon •  GPS acavated duraaon •  3G network connecaon ty

pe •  …

•  System calls •  Kernel funcaons •  Binder calls

Component Usage Monitoring (1)


“Kprobes”

INT03:Break��


Pac

ket

trans

mitt

ing

dev_queue_xmit()/netif_rx()

Time(s)

Time (tick)

Event Detector Kernel function calls

cpufreq_cpu_put()

245.0 MHz614.4 MHz806.4 MHz998.4 MHz

sched_switch()

Freq

uenc

ych

angi

ng

Pro

cess

switc

hing

Time (tick)Timing synchronization

Utilization (%)

AppScopeKernel

Process 1

Process 2

Process 3

H/W ComponentUsage Analyzer



•  Display (OLED) –  (R, G, B) and brightness

–  Accurate OLED energy esamaaon by Framebuffer Analysis

–  Event-‐based acavity transiaon monitoring •  Applicaaon-‐specific

display power esamaaon

𝑃=𝐵𝑟×𝑓(𝑅,𝐺,𝐵) ×(𝛽↓𝑅 𝑅↓𝑖 + 𝛽↓𝐺 

𝐺↓𝑖 + 𝛽↓𝐵 𝐵↓𝑖 )


Applica8on Energy Es8ma8on: Overall Framework

Device Power Modeling (Training)

Energy Es8ma8on System Evalua8on


Kernel source Behavior analysis Power Analysis Formulaaon

Error Analysis External Power Monitor

AppScope

The AppScope Project (1)

Power Models (DevScope/Vendor)

AppScopeViewer

App. Developer System Sooware Developer

Energy Bug Report / Energy Consump8on Sta8s8cs

End User

AppScopeViewer I/F

AppScope

CPU Display Wi-‐Fi Cell GPS

Simple On/Off States

Component X

Applica8ons

AppScope Library Single Core LCD OLED USPA LTE Mul8 Core …

C. Yoon, D. Kim, W. Jung, C. Kang, H. Cha, "'AppScope: Applicaaon Energy Metering Framework for Android Smartphone using Kernel Acavity Monitoring," USENIX Annual 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 Conference on Hardware/So_ware Codesign and System Synthesis (CODES+ISSS'12), 2012


Demo (AppScope/AppScopeViewer)


hdp://mobed.yonsei.ac.kr/appscope

Current Status of AppScope


Snapdragon QSD8250(1GHz)

Exynos-‐5410

Media processor(MFC) SGX 544 GPU 1.6GHz Octa. big.LITTLE

Media processor

Snapdragon 600

1.9GHz Quad.

Adreno 320 GPU 1.4GHz Quad.

2D graphics

Mali-‐400 GPU

Exynos-‐4412 3G

SKT HSDPA

WiFi

GPS

3G HSPA+

Tail Power 4G LTE

Camera

Galaxy S3

Galaxy S4 Nexus One

VEGA IRON

AMOLED Display LCD Display

AppScope Remaining Challenges


Autonomous power modeling

Thermal power modeling More components

Signal-‐strength-‐aware modeling

Sensors Touch screen Bluetooth NFC …

WiFi

4G LTE

3G HSPA+

AppScope Capabili8es & Poten8als

Energy Bugs/Hogs Detec8on

•  User behavior monitoring •  Anomaly detecaon •  Debugging frameworks

Energy Mgmt.

•  Energy management API •  BaWery virtualizaaon •  Remain capacity predicaon

•  Workload characterizaaon •  Load monitoring •  Detecang workload unbalance

Program bugs Wakelock Handover

Signal strength

•  CPU power governor •  DEVFREQ (OPP) •  Mula-‐core task scheduling

Power Mgmt.

big.LITTLE

GPU

MFC

Multicore

Task scheduling

100%

30%

30%

40%

•  Interacave UX •  Lifeame predicaon •  App. running state analysis

Energy-‐aware UX

Workload Analysis


Our Researches

Work @ Yonsei

•  Energy usage profiling •  Energy habit monitoring •  Energy bugs/hogs detecaon •  EMSOFT2013, …

•  Component usage monitoring •  Applicaaon energy esamaaon •  System energy esamaaon •  USENIX ATC’12

•  Component power modeling •  Mula-‐core CPU, GPU, OLED, … •  Online power modeling •  CODES+ISSS2012, DATE2013

AppScope

Network Env.

User Interac8on

Network Serv.

Badery State

Calling Serv.

…

…

UserScope WakeScope …

CPU Display Wi-Fi Cellular GPS

Power Management

•  Mula-‐core CPU governor •  DEVFREQ frameworks •  Personalized energy mgnt. •  Adapave duty cycling •  SenSys’11, ... Fuel-gauge IC

H/W Components

…CPU Display Wi-‐Fi Cellular GPS

BatteryDevScope

Base

p

3R 4R

Hidden State1Active State

Hidden State2

HiddenState 1

Active State

Hidden State 2


FIN

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

Technology

144 deview-2013-smartphone pm