Transcript Augmenting Mobile 3G Using WiFi

Augmenting Mobile 3G Using WiFi
Sam Baek
Ran Li
Modified from University of Massachusetts Microsoft Research
Outline
The necessity of augmenting 3G
Basic idea of Wiffler
Improvement of Wiffler and test results
Questions
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Demand for mobile access growing
Cisco Visual Networking Index: Global Mobile Data Traffic
Forecast Update, 2011–2016
global mobile data traffic will
increase 18-fold between 2011
and 2016.
All of this is understandable given
the massive adoption of mobile
devices such as smartphones.
Mobile data traffic will grow at a
compound annual growth rate
(CAGR) of 78 percent from 2011
to 2016, reaching 10.8 exabytes
per month by 2016.
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How can we reduce 3G usage?
1. Behavioral
like ATT wants to educate users by
imposing a limitation of 5GB per
month
2. Economic
Data Plan
3. Technical
Using WiFi to reduce 3G traffic
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Augmenting Mobile 3G using WiFi
Offload data to WiFi when possible
Easy to do when you are stationary
Focus on vehicular mobility
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Offloading 3G data to WiFi
Wiffler
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Basic Information
1. What is the availability of 3G and WiFi
networks as seen by a vehicular user?
2. What are the performance characteristics of
these two networks? (throughput and loss
rate)
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Measurement
 Measurement: Joint study of 3G and WiFi
connectivity
Across three cities: Amherst, Seattle, SFO
 Testbed: Vehicles with 3G modom and WiFi
(802.11b) radios


Amherst: 20 cars, Seattle: 1 car, SFO: 1 car
 Software: Simultaneously probes 3G and WiFi

Availability, loss rate, throughput
 Duration: 3000+ hours of data over 12+ days
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3G and WiFi access availability
100
90
80
70
Availability
(%)
60
50
3G
40
WiFi
30
Sum
20
10
0
Amherst
Seattle
Sfo
3G+WiFi combination is better than 3G
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Special distribution of 3G/WiFi availability
Amherst
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WiFi (802.11b) throughput is lower
Throughput = Total data received per second
WiFi
Cumulative
fraction
Upstream
3G
0.35 0.72
WiFi
Cumulative
fraction
3G
0.4 0.8
Downstream
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WiFi loss rate is higher
Loss rate = Fraction of packets lost at 10 probes/sec
Cumulative
fraction
28%
8%
WiFi
3G
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Summary
In summary, the measurement study shows that
• A non-trivial amount of WiFi is available, but is
limited around 10 percent. (3G:90%)
• Unlike stationary environments, WiFi throughput
is much lower than 3G throughput. The WiFi loss
rate performance is also poorer compared to
3G.
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Implications of measurement study
 Wiffler : simply switch from 3G to WiFi
 Drawbacks


Can offload only ~11% of the time
Can hurt applications because of WiFi’s higher loss
rate and lower throughput. (VoIP)
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Key ideas in Wiffler
Increase savings for delaytolerant applications
 Problem: Using WiFi
only when available
saves little 3G usage
 Solution: Exploit delaytolerance to wait to
offload to WiFi when
availability predicted
Reduce damage for delaysensitive applications
 Problem: Using WiFi
whenever available can
hurt application quality
 Solution: Fast switch to
3G when WiFi delays
exceed threshold
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Prediction-based offloading
D = Delay-tolerance threshold (seconds)
S = Data remaining to be sent (bytes)
Each second,
1. If (WiFi available), send data on WiFi
2. Else if (W(D) < S), send data on 3G
3. Else wait for WiFi.
Predicted WiFi
transfer size in
next D seconds
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Predicting WiFi capacity
 History-based prediction of # of APs using last
few AP encounters

WiFi capacity = (expected #APs) x (capacity per AP)
 Simple predictor yields low error both in
Amherst and Seattle
Negligible benefits with more sophisticated prediction, eg
future location prediction + AP location database
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Fast switching to 3G
 Problem:

WiFi losses bursty => high retransmission delay
 Approach:


If no WiFi link-layer ACK within 50ms, switch to 3G
Else, continue sending on WiFi
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Wiffler implementation
Wiffler
proxy
 Prediction-based offloading upstream + downstream
 Fast switching only upstream

Implemented using signal-upon-ACK in driver
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Evaluation Roadmap
 Prediction-based offloading


Deployment on 20 DieselNet buses in 150 sq. mi
region around Amherst
Trace-driven evaluation using throughput data
 Fast switching


Deployment on 1 car in Amherst town center
Trace-driven evaluation using measured loss/delay
trace using VoIP-like probe traffic
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Deployment results
Data offloaded to WiFi
30%
Wiffler’s prediction-based
offloading
WiFi when available
10%
File transfer size: 5MB; Delay tolerance: 60 secs;
Inter-transfer gap: random with mean 100 secs
Wiffler’s fast switching
WiFi when available (no switching)
% time good voice quality
68%
42%
VoIP-like traffic: 20-byte packet every 20 ms
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Trace-driven evaluation
 Parameters varied

Workload, AP density, delay-tolerance, switching threshold
 Strategies compared to prediction-based offloading:



WiFi when available
Adapted-Breadcrumbs: Future location prediction + AP
location database
Oracle (Impractical): Perfect prediction w/ future knowledge
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Wiffler increases data offloaded to WiFi
Workload: Web traces obtained from commuters
42%
14%
Wiffler close to
Oracle
Sophisticated
prediction yields
WiFi
when
negligible benefit
available yields
little savings
Wiffler increases delay by 10 seconds over Oracle.
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Even more savings in urban centers
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Fast switching improves quality of
delay-sensitive applications
73%
58%
40%
30% data offloaded to WiFi with 40ms switching threshold
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Future work
 Reduce energy to search for usable WiFi
 Improve performance/usage by predicting
user accesses to prefetch over WiFi
 Incorporate evolving metrics of cost for 3G
and WiFi usage
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Summary
 Augmenting 3G with WiFi can reduce pressure on
cellular spectrum
 Measurement in 3 cities confirms WiFi availability
and performance poorer, but potentially useful
 Wiffler: Prediction-based offloading and fast
switching to offload without hurting applications
Questions?
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Error in predicting # of APs
Relative
error
N=1
N=4
N=8
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Fast switching improves performance
of demanding applications
% time with
good voice
quality
Oracle
Only 3G
Wiffler
No switching
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