Into the Wild: Studying Real User Activity Patterns to Guide Power Optimizations for Mobile Architectures Alex Shye, Benjamin Scholbrock, and Gokhan Memik Northwestern University Electrical Engineering and Computer Science Department Energy Efficiency – ELEC 518 Spring 2011 Jash Guo, Myuran Kanga Rice University Houston, TX Mar 17, 2011

2 Agenda Background The Paper –Introduction –Experiment –Findings –Evaluation –Conclusions Related Works/Topics Page 2

3 Page 3 Background Venue –Proceedings of the 42nd Annual IEEE/ACM International Symposium on Microarchitecture –MICRO 2009: December 12-16, 2009 –52 out of 209 submissions

4 Page 4 Gokhan Memik Associate Professor EECS, Northwestern Alex Shye PhD Student 2010’ EECS, Northwestern Ben Scholbrock PhD Student EECS, Northwestern

Introduction ● Increased need for mobile computing ● Batch jobs/Long running services disabled – iPhone ● End-user activity (Workload) ● Android G1 logger – User power consumption ● CPU frequency scaling/Screen Brightness Page 5 content/uploads/2010/06/iphone4_2up_angle.jpg brain-computer-interface/

Experiment Architecture – HTC Dream Power Estimation Model – Using real measurements Logger application Deployment Useful data Page 6 High-level overview of the target mobile architecture

Power Model/Building Estimation Power states: Active/Idle Choosing parameters Estimation model build Real-time Measurements R-tool – Linear Regression Model Page 7 Parameters used for linear regression in power estimation model

Model Validation Additional logs recorded Strict hardware Scenario Accurate power estimation – Median 6.6% Page 8 Cumulative total energy error Cumulative distribution of power estimation error

Per Component Power Measured and predicted power consumption Surfing the internet and streaming media for 160sec Actual usage varies by workload Similar breakdown for all components (next slide image) Page 9 Power Consumption Timeline

Power Breakdown Idle time a significant issue Varying solutions based on workload Summary Accurate total system power estimation Power breakdown – Highly dependent on workload Page 10 User power breakdown User power breakdown excluding idle time

Idle Time “Energy Efficiency of Handheld Computer Interfaces: Limits, Characterization and Practice,” Lin Zhong and Niraj K. Jha Department of Electrical Engineering - Princeton University Human sensory limits Speech recognition rates vs. typing Interface cache User acceptance Page 11 Interface cache wrist-watch device

12 Page 12 Findings The end user is the workload Variation in the power break-down between users The CPU and the screen are the two most power-consuming components

13 Page 13 Characterizing Real User Workloads The workload of a mobile architecture has a large effect on its power consumption The hardware components that dominate power consumption vary drsticaly depending upn the workload The user determines the workload for a mobile architecture

14 Page 14 Power Breakdown Including Idle Time

15 Page 15 Power Breakdown Excluding Idle Time

16 Page 16 The Paper Focus on Active State Idle State (about 68 mW) Active State (up to 2000 mW) Active state contributes highly to the user experience Active state accounts for 50.7% of the total system power

17 Page 17 Screen Usage of Real Users Screen Interval: a continuous block of time where the screen is on Duration: the length of time corresponding to the interval 70% of total screen duration > 100s The total duration time is dominated by a relatively small percentage of long screen intervals

18 Page 18 User-Aware Optimizations A few long screeen intervals dominate the overall screen duration time The power consumption during Active time is dominated by the screen and the CPU Change Blindness: the inability for humans to detect gradual/large changes in their surrounding environment

19 Page 19 Change Blindness

20 Page 20 Solutions Develop an accurate estimation model Slowly decrease CPU frequency Slowly decrease screen brightness

21 Page 21 CPU Optimization Dynamic frequency scaling (DFS) algorithm ondemand DFS governor

22 Page 22 Screen Optimization Decrease the brightness by 7 units every 3 seconds until 60% threthold Affect only long screen inervals Maintain user perception

23 Page 23 Experimental Results Screen Ramp CPU Ramp Screen Drop CPU Drop Emulate the optimizations on the user logs Conduct a user study

Power savings User satisfaction Evaluation with blind use of optimizations Single run evaluations Page 24 Results/Evaluation Total system power savings for each of the optimizations as estimated by our power model

Page 25 User Satisfaction Reported user satisfaction

Feedback and Solution Acceptance User disclosure – Screen/CPU significance Feedback based on input response CPU frequency change – Jitter Change blindness beneficial Optimization On/Off tool? User pattern essential to proper power consumption reduction Page 26 Glitchy Screen sizes/l/in/set /

27 Page 27 Conclusion Mobile architectures – natural environment Logger application to collect logs Develop power estimation model Findings show CPU and screen dominate usage Optimizations based on user behavior Change blindness utilized for 10% total savings

28 Page 28 Benefits/Criticisms Pros –Estimation Model –The Logger –Real Users –Real Patterns –Usage interval awareness –Change Blindness Cons –Linear? –Logger Overhead –Sample Size Single model 20 users 145/250 days –Device/User Gap –Major focus on CPU –Future Trends More WiFi, EDGE

29 Page 29 Related Paper Power to the People: Leveraging Human Physiological Traits to Control Microprocessor Frequency (2008) Power saving by better understand the individual user satisfaction

30 Page 30 Related Paper Energy Efficiency of Handheld Computer Interfaces: Limits, Characterization and Practice(2005) Utilize interface cache for small tasks Typical text entry speeds

31 Page 31 Related Paper Energy-aware adaptation for mobile applications (1999) Tradeoff between energy conservation and application quality

32 Page 32 Related Paper Human Generated Power for Mobile Electronics (2004) Alternatives to batteries: additional power sources Human power generation

33 Page 33 Human Factors in Power Savings Source –Better batteries –Additional power sources Hardware Software Monitoring Alarming Perception –User satisfaction –Quality vs. performance sacrifice Human power generation – Proof of concept