Post on 17-Dec-2015
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
2
The Urban Tomography System
Motivation Problem Solutions Approach Evaluation Conclusion
Video Collection 10 years ago
Our way of Video Collection
3
Users
Motivation Problem Solutions Approach Evaluation Conclusion
Transportation SecurityPost-Disaster Urban Planning
Documenting Post-Katrina
Reconstruction
4
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
5
Our Focus
Motivation Problem Solutions Approach Evaluation Conclusion
Transferring Large Volumes of Data
Leveraging Delay Tolerance
Reduce the energy cost
6
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
7
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
8
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?
9
Our Approach
Motivation Problem Solutions Approach Evaluation Conclusion
Use Lyapunov Optimization
Derive Control Algorithm
10
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
11
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
12
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
13
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
14
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?
15
SALSA Summary
Whether, When, Which
Delayed Transmission
TunableDelay-Tolerance
SALSA
Motivation Problem Solutions Approach Evaluation Conclusion
16
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
17
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
18
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
19
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
20
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
21
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
22
The Bottom Line
Additional Delay
Gain? Loss?Save 2% ~ 80% of battery capacity
+ 2 min ~ 2 hour
Energy Savings
Battery
23
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)
24
Thank you. Questions?