Energy Efficiency – ELEC 518 Spring 2011
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
Jash Guo, Myuran Kanga
Rice University
Houston, TX
Mar 17, 2011

• Background
• The Paper
– Introduction
– Experiment
– Findings
– Evaluation
– Conclusions
• Related Works/Topics
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• Venue
– Proceedings of the 42nd Annual IEEE/ACM
International Symposium on Microarchitecture
– MICRO 2009: December 12-16, 2009
– 52 out of 209 submissions
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Gokhan Memik
Associate Professor
EECS, Northwestern
Alex Shye
PhD Student 2010’
EECS, Northwestern
Ben Scholbrock
PhD Student
EECS, Northwestern
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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://rdn-consulting.com/blog/2007/12/21/bcibrain-computer-interface/ http://www.mobilecrunch.com/wpcontent/uploads/2010/06/iphone4_2up_angle.jpg

• Architecture – HTC Dream
• Power Estimation Model –
Using real measurements
• Logger application
• Deployment
• Useful data
High-level overview of the target mobile architecture
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• Power states: Active/Idle
• Choosing parameters
• Estimation model build
• Real-time Measurements
• R-tool – Linear Regression Model
Parameters used for linear regression in power estimation model

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• Additional logs recorded
• Strict hardware
• Scenario
• Accurate power estimation –
Median 6.6%
Cumulative distribution of power estimation error
Cumulative total energy error

• 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)
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Power Consumption Timeline

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

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“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
Interface cache wrist-watch device

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• 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
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• 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
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• 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
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• 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
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• 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
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• Develop an accurate estimation model
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• Slowly decrease CPU frequency
• Slowly decrease screen brightness
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• Dynamic frequency scaling (DFS) algorithm
• ondemand DFS governor
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• Decrease the brightness by 7 units every 3 seconds until 60% threthold
• Affect only long screen inervals
• Maintain user perception
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• Screen Ramp
• CPU Ramp
• Screen Drop
• CPU Drop
• Emulate the optimizations on the user logs
• Conduct a user study
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• Power savings
• User satisfaction
• Evaluation with blind use of optimizations
• Single run evaluations
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Total system power savings for each of the optimizations as estimated by our power model

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Reported user satisfaction

• 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
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Glitchy Screen http://www.flickr.com/photos/aparrish/5515150358/ sizes/l/in/set-72157626237465468/

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• 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
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• 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
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• Power to the People: Leveraging Human
Physiological Traits to Control
Microprocessor Frequency (2008)
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Power saving by better understand the individual user satisfaction
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• Energy Efficiency of Handheld Computer
Interfaces: Limits, Characterization and
Practice(2005)
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Utilize interface cache for small tasks
Typical text entry speeds
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• Energy-aware adaptation for mobile applications (1999)
Tradeoff between energy conservation and application quality
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• Human Generated Power for Mobile
Electronics (2004)
Alternatives to batteries: additional power sources
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Human power generation
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• Source
– Better batteries
– Additional power sources
• Hardware
• Software
• Monitoring
• Alarming
• Perception
– User satisfaction
Human power generation – Proof of concept
– Quality vs. performance sacrifice
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