Week 3 ETEC 668 Quantitative Research in Educational Technology Dr. Seungoh Paek January 29, 2014.
Energy-Delay Tradeoffs in Smartphone Applications Moo-Ryong Ra Jeongyeup Paek, Abhishek B. Sharma...
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Energy-Delay Tradeoffs in Smartphone Applications Moo-Ryong Ra Jeongyeup Paek, Abhishek B. Sharma Ramesh Govindan, Martin H. Krieger, Michael J. Neely University of Southern California MobiSys’10
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Transcript of Energy-Delay Tradeoffs in Smartphone Applications Moo-Ryong Ra Jeongyeup Paek, Abhishek B. Sharma...
Energy-Delay Tradeoffs in Smartphone Applications
Moo-Ryong RaJeongyeup Paek, Abhishek B. Sharma
Ramesh Govindan, Martin H. Krieger, Michael J. Neely
University of Southern California
MobiSys’10
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The Urban Tomography System
Motivation Problem Solutions Approach Evaluation Conclusion
Video Collection 10 years ago
Our way of Video Collection
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Users
Motivation Problem Solutions Approach Evaluation Conclusion
Transportation SecurityPost-Disaster Urban Planning
Documenting Post-Katrina
Reconstruction
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Delay-Tolerance
Motivation Problem Solutions Approach Evaluation Conclusion
Delay-Tolerance
Many of our users are delay-tolerantTransportation Security
Dealing withChild development
Issues
Planning Research
But tolerance varies considerably
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Our Focus
Motivation Problem Solutions Approach Evaluation Conclusion
Transferring Large Volumes of Data
Leveraging Delay Tolerance
Reduce the energy cost
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EDGE/3G WiFi
Energy(J/bit)
Availability
ChannelQuality
Trade-offs
Motivation Problem Solutions Approach Evaluation Conclusion
HIGH LOW
HIGH LOW
Time-Varying Time-Varying
Delay transmission
Adapt to wireless channel quality
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A Motivating Example
Motivation Problem Solutions Approach Evaluation Conclusion
EDGE 3G WiFi
video 1arrives
video 2arrives
40 KB/s
200 KB/s
50 KB/s
10 KB/s
TIMETIME
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TIMETIME
Strawman Approaches
Motivation Problem Solutions Approach Evaluation Conclusion
EDGE 3G WiFi
video 1video 2
DelayEnergy
246242
95
305
50
320
J sec
Min-DelayWiFi-OnlyEnergy-Optimal
Optimal can save significant energy
MD ME EO MD ME EO
Challenge: How to design the optimal trade-off algorithm?
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Our Approach
Motivation Problem Solutions Approach Evaluation Conclusion
Use Lyapunov Optimization
Derive Control Algorithm
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Lyapunov Optimization
Motivation Problem Solutions Approach Evaluation Conclusion
Sender Receiver
Control Algorithm
Lyapunov Analysis
1. Queue Stability
Penalty Function 2. Penalty Minimization
Queue length will not go to
the infinity
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Energy CostModel
How we use the framework
Motivation Problem Solutions Approach Evaluation Conclusion
Sender Receiver
ControlAlgorithm
Lyapunov Analysis
1. Queue Stability
2. Penalty Minimization
1. Delay Bound
2. Energy Minimization
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Higher rate
Derived Control Decision
Motivation Problem Solutions Approach Evaluation Conclusion
SALSA (Stable and Adaptive Link Selection Algorithm)
AP
Q Large Queue Backlog
E Low Energy Cost
QueueBacklogMAX × − ×Estimated
RateEnergy
CostVOver
All Links
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Importance of V
QueueBacklogMAX × − ×Estimated
RateEnergy
CostV
SALSA (Stable and Adaptive Link Selection Algorithm)
Setting VLarge
Defer theTransmission
Motivation Problem Solutions Approach Evaluation Conclusion
Over All Links
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Tuning Delay-Tolerance
Motivation Problem Solutions Approach Evaluation Conclusion
NoNo
NoNo
NoNo YesYes TIMETIME
AP RateAP Rate
NoNo YesYes
VV
Queueing De-lay
Queueing De-lay
Transportation Security
Urban Documentation
Transmit?Transmit?
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SALSA Summary
Whether, When, Which
Delayed Transmission
TunableDelay-Tolerance
SALSA
Motivation Problem Solutions Approach Evaluation Conclusion
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Evaluation Methodology
Motivation Problem Solutions Approach Evaluation Conclusion
Trace-DrivenSimulation
Validation with Real Implementation
Implementationon Nokia N95
66 Link availability traces
42 Video arrival traces
2772USC LAX Mall
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Metric
Motivation Problem Solutions Approach Evaluation Conclusion
E(J/byte)
D(H
our\
byte
)
E(J/byte)
D(H
our)
dispersion
Envelope in E-D Plane Dispersion
Good!
BAD Measured
Optimal
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Comparisons
Motivation Problem Solutions Approach Evaluation Conclusion
Min-delay High Energy
WiFi-only Unbounded delay
Static-delayNOT take link
quality into account
Know-WiFiNOT consider queue backlog
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Main Results
Motivation Problem Solutions Approach Evaluation Conclusion
Ignore link quality
SALSAKnow-WiFiStatic-DelayWiFi-onlyMin-Delay
Ignore queue backlog
Since SALSA takes all factors into account, it performs closest to the optimal
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Tuning Delay-Tolerance
Motivation Problem Solutions Approach Evaluation Conclusion
Decay Vslowest
Decay Vfastest
SALSA can be tuned to differentdelay-tolerant requirements.
More Delay-Tolerant Less Delay-Tolerant
Like WiFi-only Like Min-Delay
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Experimental Validation
Motivation Problem Solutions Approach Evaluation Conclusion
Glendale USC
Simulation Results Experiment Results Our experimental results fall
within the boundary and validate the simulation
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The Bottom Line
Additional Delay
Gain? Loss?Save 2% ~ 80% of battery capacity
+ 2 min ~ 2 hour
Energy Savings
Battery
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Summary of Contributions
Motivation Problem Solutions Approach Evaluation Conclusion
Different from prior work• [Balasubramanian10], [Zaharia07], [Seth06] consider schemes
similar to Static-Delay and Know-Wifi.• [Rahmati07], [Armstrong06], [Agarwal07] consider
link selection, but do not defer transmissions
Adaptive algorithm for energy/delay tradeoff• Extensive evaluation with real world scenarios• Validation with real implementation• Provable performance bound (in the paper)
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Thank you. Questions?
