Augmenting Mobile 3G Using WiFi
Sam BaekRan 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
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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.
How can we reduce 3G usage?
1. Behavioral
2. Economic
3. Technical
4
like ATT wants to educate users by imposing a limitation of 5GB per month
Data Plan
Using WiFi to reduce 3G traffic
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
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Wiffler
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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8
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
3G and WiFi access availability
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Availability (%)
3G+WiFi combination is better than 3G
Amherst Seattle Sfo0
10
20
30
40
50
60
70
80
90
100
3GWiFiSum
Special distribution of 3G/WiFi availability
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Amherst
WiFi (802.11b) throughput is lower
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Cumulative fraction
Cumulative fraction
WiFi
3G
WiFi
3G
Upstream
Downstream
0.35 0.72
Throughput = Total data received per second
0.4 0.8
WiFi loss rate is higher
12
Cumulative fraction WiFi
3G
28%
8%
Loss rate = Fraction of packets lost at 10 probes/sec
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 WifflerIncrease savings for delay-
tolerant applications Problem: Using WiFi
only when available saves little 3G usage
Solution: Exploit delay-tolerance to wait to offload to WiFi when availability predicted
Reduce damage for delay-sensitive applications
Problem: Using WiFi whenever available can hurt application quality
Solution: Fast switch to 3G when WiFi delays exceed threshold
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 3G3.Else wait for WiFi.
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Predicted WiFi transfer size in next D seconds
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Negligible benefits with more sophisticated prediction, eg future location prediction + AP location database
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
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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
Wiffler implementation
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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
Deployment resultsData offloaded to WiFi
Wiffler’s prediction-based offloading 30%WiFi when available 10%
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% time good voice quality Wiffler’s fast switching 68%
WiFi when available (no switching) 42%
File transfer size: 5MB; Delay tolerance: 60 secs; Inter-transfer gap: random with mean 100 secs
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
Wiffler increases data offloaded to WiFi
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Workload: Web traces obtained from commuters
Wiffler increases delay by 10 seconds over Oracle.
42%
14%
Wiffler close to OracleSophisticated prediction yields negligible benefitWiFi when available yields little savings
Even more savings in urban centers
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Fast switching improves quality of delay-sensitive applications
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40%58%
73%
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?
Error in predicting # of APs
30
Relative error
N=1
N=4N=8
Fast switching improves performance of demanding applications
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% time with good voice
quality
OracleOnly 3GWifflerNo switching
