Augmenting Mobile 3G Using WiFi
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Augmenting Mobile 3G Using WiFi
Sam BaekRan Li
Modified from University of Massachusetts Microsoft Research
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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.
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How can we reduce 3G usage?
1. Behavioral
2. Economic
3. Technical
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like ATT wants to educate users by imposing a limitation of 5GB per month
Data Plan
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
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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
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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
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Special distribution of 3G/WiFi availability
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Amherst
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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
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WiFi loss rate is higher
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Cumulative fraction WiFi
3G
28%
8%
Loss rate = Fraction of packets lost at 10 probes/sec
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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 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
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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 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
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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
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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
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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
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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?
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Error in predicting # of APs
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Relative error
N=1
N=4N=8
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Fast switching improves performance of demanding applications
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% time with good voice
quality
OracleOnly 3GWifflerNo switching