PowerForecaster: Predicting Smartphone Power Impact of Continuous Sensing Applications at Pre-installation Time

Transcription

1 PowerForecaster: Predicting Smartphone Power Impact of Continuous Sensing Applications at Pre-installation Time Chulhong Min 1 Youngki Lee 2 Chungkuk Yoo 1 Seungwoo Kang 3 Sangwon Choi 1 Pillsoon Park 1 Inseok Hwang 4 Younghyun Ju 5 Seungpyo Choi 1 Junehwa Song 1 1 KAIST 2 Singapore Management University 3 KOREATECH 4 IBM Research Austin 5 Naver Labs

2 Continuous Sensing Applications Activity monitoring [Lester et al. UbiComp 09] Transport mode monitoring [Hemminki et al. SenSys 13] Conversation monitoring [Lee et al. MobiSys 13] Sleep quality monitoring [Hao et al. SenSys 13]

3 Continuous Sensing Applications Activity monitoring [Lester et al. UbiComp 09] Transport mode monitoring [Hemminki et al. SenSys 13] Conversation monitoring [Lee et al. MobiSys 13] Sleep quality monitoring [Hao et al. SenSys 13]

4 Developer User comments Ambiguous description Inconsistent user experiences Inaccurate

5 What if the application market provides the personalized power consumption at pre-installation time?

6

7 It s What not if the application easy to market realize provides the in personalized reality!! power consumption at pre-installation time?

8 Hours Power Use of an App Varies Noticeably Power use analysis from 27 users over 3 weeks - 4 undergraduates, 7 company employees, 5 graduates, 1 housewife - Conversation monitoring application Conversation monitoring duration Participant ID All different!! Average, stdev.: 4.2 hours, 2.2 hours Top user: 3.5 hours more than the average 7.5 hours more than bottom user

9 Why Different? Diversity in individual user behavior: Physical user activities, phone use patterns Conditional sensing pipeline for energy saving Shared use of resources with foreground applications Yes With somebody? No Start monitoring Stop monitoring

10 PowerForecaster Personalization Installation-free PowerForecaster: a system to provide the personalized power consumption of an application at pre-installation time Responsive Low overhead This app will decrease the battery life of your phone by 2 hours. No need to modify the app

11 User Behavior-aware Power Emulation Output: power H/W usage tracking App execution Sensor trace Device usage trace Physical activity Phone use pattern

12 Sensor Trace Collection Goal: to account the power consumption of the sensing application on the emulator Sensor data streams with timestamps sensing apps input System calls and callbacks power states in the sensor devices Sensor-related system events triggers of internal logic Sensor Type Sensor Data Related System Call Related System Event GPS Bluetooth longitude, latitude, altitude, speed, bearing, name, address, bond state, type, UUIDs, RSSI gps_start(), gps_stop(), startdiscoverynative(), stopdiscoverynative(), Accelerometer values enablesensor(), disablesensor() GPS_EVENT_STARTED, GPS_EVENT_STOPPED, ACTION_FOUND, ACTION_DISCOVERY _STARTED/_FINISHED

13 Device Usage Trace Collection Goal: to consider the resource sharing with existing applications Execute foreground applications directly on the emulator? No. heavy computation, difficult to log and replay realistic inputs Our approach: record and replay the requests to various H/W components (e.g., acquire() / release()) Resource type Request APIs Parameters CPU acquire() / release() on WakeLock setrepeating() / cancel() on AlarmManager GPS requestlocationupdates() / removelocationupdates() on LocationManager Bluetooth startdiscovery() / stopdiscovery() on BluetoothAdapter timeout, reference counted alarm type, trigger time, trigger interval provider, mindelay, mindistance, criteria

14 Error rate (%) Power cost (mw) Energy-efficient Trace Collection Collect the trace once, and use for generic emulation Duty-cycling collection Human contexts have temporal locality How far we can increase the cycle with minimal decrease in power estimation accuracy? Explorative study with a location tracking application Two 18-hour long traces Active data collection: 2 min Error rate Power cost Ratio of 1/12: estimation error is still below than 10% and power cost starts to saturate Duty cycle period (minutes) 50 0

15 Android Framework User Behavior-aware Power Emulator Sensor Manager sensor data BT/WiFi Manager Sensing app Sensor Emulator Location Manager Accel. Accel. BT Wi-Fi GPS trace H/W Usage Monitor Power Manager Device Usage Replayer trace Alarm Manager Power Estimator wake-up msg. Kernel Step1. Sensor emulation Step2. Power estimation Step3. Accounting shared use

16 Step 1: Sensor Emulation Android emulator PowerForecaster with sensor emulator

17 Step 1: Sensor Emulation Example with GPS register(10sec) Location tracking app Coordinate1 Coordinate2 Sensor emulator GPS_EVENT_STARTED GPS emulator trace Accel trace Gyro trace BT scan trace Wi-Fi scan trace gps_on() gps_off() Kernel gps_on() gps_off() t1: gps_on() t2: gps_off() t10: gps_on() H/W usage monitor

18 Step 2: Power Estimation Hardware usage monitor: A collection of system calls package = null flags = 1 tag = AlarmManager uid = 1000 pid = 245 lock = package = null flags = 1 tag = AlarmManager uid = 1000 pid = 245 lock = package = null flags = 1 tag = AlarmManager count = 1 refcounted = true lock = package = com.example.intelligps alarm = Alarm{41e85670 type 2 com.example.intelligps} intent = package = null flags = 1 tag = AlarmManager count = 0 refcounted = true lock = package = null flags = 0 death = false lock = package = null flags = 1 tag = *vibrator* uid = 1000 pid = 245 lock = Power estimation: Finite state machine (FSM)-based power model for {inertial sensors, GPS, BT scan, Wi-Fi scan, microphone, CPU} Overall 93% of accuracy Base 0mW Inactivity for 5s GPS State 3 253mW GPS On State1 422mW GPS On GPS GPS On off State 2 422mW Inactivity for 4s Base 0mW Inactivity for 2s State 3 302mW BT scan State1 368mW Cancel Inactivity for 4s State 2 113mW Inactivity for 5s Bluetooth

19 Step 3: Accounting Shared Use Sensing app Device usage replayer trace Power use while only a sensing app uses resources Power cost by the sensing app Power use while resources are shared Ignore (We assume this is due to existing apps) Hardware usage monitor Phone-wide hardware usage Power accounting Net power increase by a sensing app Without consideration, the error comes up to 46%

20 One More Challenge Long emulation time!! The naïve emulation for one-day long trace takes one-day.

21 Final Error rate Effect of Acceleration Techniques 120% 80% Accupedo ChatMon MyPath 40% Accupedo, MyPath: Error saturates to 10% around within 20 sec. 0% ChatMon (except an outlier): Error saturates to 5% around within 10 sec. Approximation time (sec)

22 Idle Time Skipping Characteristics of sensing applications Sensing applications try to keep the device in sleep state Key idea: Detect and skip the idle time Before acceleration Accupedo Accel. sensor collect and process accel. data (0.02 sec) idle period (9.98 sec) movement detected time Idle time skipping

23 Experimental Setting Overview of the experiments Power estimation accuracy in realistic settings Real deployment System overhead Phones, emulators, and servers Nexus S (Android 4.1.2), Nexus 5 (4.4.4) Desktop server with i CPU and 16GB RAM Sensing applications Three commercial pedometers (Accupedo, NoomWalk, Pedometer2.0) Two research applications MyPath: movement-based location tracker ChatMon: encounter-based conversation monitor

24 Experimental Setting Crafted 5 one-hour user behavior scenarios Parameters: mobility, indoor/outdoor status, encounter, phone usage Determined parameter values based on real user data ID User Activity Time Mobility Moving Moving Indoor Encounter Graduate student App (M, 30s) usage Undergraduate student (M, 20s) Office worker (F, 30s) Office worker (M, 20s) Housewife (F, 50s) User behavior parameter Indoor Encounter Moving Indoor Encounter App usage Shopping alone 50 min 60 min We 0 min 5 min Map b Web Video Moving and class 10 min 45 min 15 min 20 min Moving and lunch 20 min 40 min 60 min 15 min Going out 30 min 30 min 40 min 30 min Going out 30 min 30 min 40 min 1 min

25 Experimental Setting Measurements: PowerForecaster: power estimation on the emulator Ground truth: net power increase of the sensing application Global-average: average power of sensing apps over all scenarios.

26 Net power increase (mw) Accuracy of PowerForecaster Average error rate for 3 applications and 5 scenarios PowerForecaster: only 6.6% Global-average: 99% Accupedo Scenario ID PowerForecaster: accurate power estimation for different user behavior Ground truth Global-average PowerForecaster

27 Net power increase (mw) Accuracy of PowerForecaster Average error rate for 3 applications and 5 scenarios PowerForecaster: only 6.6% Global-average: 99% MyPath ChatMon Scenario ID Scenario ID

28 Net power (mw) Net power (mw) More Apps and Phones For more commercial applications: Noom Walk, Pedometer2.0 For newer phone: Nexus 5 (4.4.4) 300 Need for power comparison Accurate power estimation 50 0 on newer phone 0 NoomWalk Pedometer2.0 Ground truth PowerForecaster Global-single(Scenario4, (a) more apps (b) Nexus Accupedo MyPath ChatMon

29 Battery decrease (hr.) Real Deployment 7 participants Nexus S with participant s own SIM card and the apps they usually use 2-week data collection First-week: without sensing app Second-week: with sensing app Battery life decrease (hours): power impact of sensing applications Different power impact even for the same app Accupedo MyPath ChatMon 10 Accupedo reduces by hours 5 0 P1 P2 P3 P4 P5 P6 P7

30 Estimation Accuracy Pick two days during the 1st week and estimate the battery life Compare the estimation with the 2nd-week battery life Result: over 90% of accuracy for 10 cases out of Accupedo MyPath ChatMon Irregular behavior 0 P1 P2 P3 P4 P5 P6 P7 2nd-week avg. Estimation with day1 Estimation with day2

31 P4 s Expected Power Hotspot of MyPath

32 System Overhead Mobile-side cost for one-time collection Setting: 1/12 duty cycle Accelerometer/light/proximity/magnetic/gyroscope (delay_fastest) GPS/BT/3G/Wi-Fi (1min.) Expected battery decrease: 1.1 hours for Nexus S and 0.6 hours for Nexus 5 Storage: 35 MB Server-side cost for one-time emulation Network/storage cost: 35MB CPU utilization: 5% Memory: 400MB ( virtual phone image) Power computation: negligible, <1sec.

33 Limitations and Future Work More personalized estimation Representative user behavior traces Server-side scalability Accommodating multiple sensing applications on a single emulator Heterogeneous phones Adopting self-constructive power models

34 Conclusion User behavior: key to understand power impact of continuous sensing applications PowerForecaster: a system to provide personalized power consumption of an application at pre-installation time User behavior-aware power emulation Acceleration of emulation PowerForecaster achieves 93% accuracy under 20 realistic workloads.

35 Thank You Any Questions?