Embed Size (px)
Citation preview
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 KangaRice UniversityHouston, TXMar 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 4Gokhan 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
http://www.mobilecrunch.com/wp-content/uploads/2010/06/iphone4_2up_angle.jpg
http://rdn-consulting.com/blog/2007/12/21/bci-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 Screenhttp://www.flickr.com/photos/aparrish/5515150358/sizes/l/in/set-72157626237465468/
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
