Smartphone Background Activities in the Wild: Origin, Energy Drain, and Optimization

Outline Introduction Related Work Characterizing Background Activities in the Wild Trace Collection Screen-off Intervals Background Activities in Screen-off Energy Drain Methodology Background Energy Analysis Key Idea BFC Analysis Conclusion

Introduction Many apps on smartphones wake up periodically to run when users are not actively interacting with them. to perform a refresh of the app state to sync with cloud services and get status updates or notifications to support non-touch based user interactions On average 28.9% of the daily energy drain is due to apps and services running during screen-off intervals. (1) e.g., in apps that provide updates for news, financial stocks, weather or twitter feed updates, (2) e.g., in social networking apps such as Facebook, WeChat, and Google+, (3) such as in audio apps Pandora and Spotify which periodically download files to play songs, or apps used for location tracking or navigation which allow a user to listen to directions without screen interactions.

Introduction Turn off all the background activities. iOS: Disable Background Refresh Android: Restrict Background Data Disable useful background activities, affecting user experience Disable background activities respectively.

Introduction Background activities, foreground activities, and user experience. The usefulness of background activities of an app is likely to be user-dependent. In this paper, we explore effective ways of optimizing background activities of apps and services. Our exploration is motivated by two hypotheses:

Introduction Background to Foreground Correlation(BFC). The level of background activities of an app should be personalized to individual users. A screen-off energy optimizer on Android: HUSH. Saves screen-off energy of smartphones by 15.7% on average. 1. A metric to measure usefulness 2. Experiment 3. HUSH monitors the BFC of all apps on a phone online and automatically identifies and suppresses app background activities during screen-off intervals that are not useful to the user experience. 4. HUSH saves screen-off energy of smartphones by 15.7% on average with minimal impact on the user experience with the apps.

Related Work Most focus on the smartphone app traffic. Falaki et al. characterized smartphone traffic based on traffic traces and showed how power consumption could be reduced Huang et al. collected traffic and studied screen-off radio energy consumption None of the work provided a thorough analysis of how energy is consumed on the smartphones in the wild. Only a few have looked at the impact of background traffic activities on the power consumption.

Trace Collection Coarse-grained logging Fine-grained logging On-demand event logging Trace collection Logging overhead CPU usage network usage dynamic events: screen being switched on and off,WiFi being associated and scanning,WiFi and cellular signal strength change, battery level change (1% granularity) and the time each app starts and stops. 2000 Galaxy S3 and S4 devices; 2.4% of the total CPU time, 0.3% of the total network bytes, and 0.6% of the total energy drain.

Screen-off Intervals The duration of screen-on/off intervals differ significantly. The screen-off intervals tend to last much longer than screen-on intervals.

Background Activities in Screen-off A longer screen-off interval does not necessarily imply more background activities and more energy drain. 9.8 vs 16.7 50.8 vs 22.7

Energy Drain - Methodology A hybrid power model power draw has linear correlation with utilization FSM-based modeling for wireless interfaces such as WiFi/3G/LTE The component power draw are largely independent, and hence the total power draw can be approximated by summing the power draw of individual components.

Energy Drain – Background Energy Analysis Overall screen-on vs. screen-off Back-ground app/service energy in screen-off CPU idle energy in screen-off Overall screen-on vs. screen-off: 17+6+22.9=45.9% of the total energy drain in a day occurs during screen-off Back-ground app/service energy in screen-off: 6+22.9=28.9% screen off 11.1% screen on CPU idle energy in screen-off: 50.8% of the total CPU time in idle in screen-off, but it only drains on average 6.0% of the total energy

Key Idea Not all of the background activities are necessary Usefulness of app screen-off activities is app-dependent user-dependent Premise Background activities of apps are meant to improve user app experience but they are only useful if the user interacts with those apps in foreground some time during the next screen-on interval. The usefulness of background activities of an app is likely to be user-dependent and thus their occurrences should be personalized. Whether it would run in the foreground during the next screen-on interval.

Outline How to quantify usefulness? Test the hypothesis How to develop an online algorithm to optimize screen-off energy?

Quantify Usefulness: Background-Foreground Correlation (BFC) 1. Define per-interval Screen-off interval Screen-on interval Background activity Foreground activity b1 b2 time 2. BFC is the average of 0  low correlation  useless 1  high correlation  useful

BFC of 2000-User Traces BFC is app-dependent BFC is user-dependent 60% of apps have zero BFC BFC is user-dependent

Stability of BFC The BFC for the same app on the same device is fairly steady or changes slowly BFC for the same app may vary sharply across users, it is fairly stable for individual users.

Prediction-based Online Algorithm 1. Keep track of per-app BFC for each user using exponential moving average , 2. Suppress background activities in interval if

Choosing Parameters 1. 22% apps have more than 10 background activities per day 2. 10% apps have more than 36 background activities per day 3. 77% of apps have more daily background activities than daily foreground activities

Choosing Parameters Case 1: there are few foreground activities and hence many background activities per foreground activity (e.g., Google Now, and Gmail).

Choosing Parameters Case 2: there are many foreground activities and hence few background activities per foreground activity (e.g., Facebook-User 2, Whatsapp-User 2).

Choosing Parameters Case 3: the app contains alternating phases with few and many foreground activities (e.g., Facebook-User 1, Whatsapp-User 1).

Evaluation Metrics 1. Energy saving: 2. Staleness: time Background activity Foreground activity

Evaluation of Prediction-based Online Algorithm 2.5x staleness increase 16.4% avg. energy saving (upper bound = 29%) Can we improve staleness and maintain energy saving?

Analysis of High Staleness

Exponential Backoff Algorithm Original algorithm： Foreground activity Background activity time staleness Relax the strictness of suppressing Exponential backoff： time threshold time:

Exponential Backoff Algorithm Original algorithm： Foreground activity Background activity time staleness Relax the strictness of suppressing Exponential backoff： time staleness

Evaluation of Exponential Backoff Algorithm staleness increase 2.5x 1.3x avg. energy saving 16.4%  15.7% staleness of individual apps reduces

LocationManagerService Architecture of HUSH ActivityManagerService BatteryStatsImpl.Uid.Pkg.Serv updateBg updateFg Intercept framework modules to suppress background activities on behalf of apps allowHush LocationManagerService TelephoneRegistry PendingIntentRecord BroadcastQueue … BatteryStatsImpl.Uid.Pkg{ long mBgTime; long mThrTime; void updateFg(){…} void updateBg() {…} boolean allowHush() {…} }

Early Evaluation of HUSH 2 Users: 3 days with original Android, 3 days with HUSH User - 1 User - 2 Number of installed apps 73 52 Daily screen-on intervals 85 29 Daily screen-on time (min) 82.35 49.95 Daily suppressions by HUSH 4400 5543 Android HUSH Daily CPU busy time (min) 164.2 97.40 60.81 27.24 Maintenance power (mA) 12.76 12.12 Avg. screen-off power (mA) 15.57 5.27  3.19 2.18  Avg. screen-on power (mA) 316.8 323.5  271.4 273.0  Overall avg. power (mA) 45.50 36.34  27.32 18.99  3x 1.5x 1.3x 1.4x

Conclusion Energy measurement study in the wild 29% of daily energy due to background activities during screen-off Quantify usefulness of background activities Background-Foreground Correlation Usefulness is app-dependent and user-dependent Screen-off energy optimizer: HUSH Save 15.7% daily energy on average Available at https://github.com/hushnymous/