Augmenting Mobile 3G Using WiFi

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

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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

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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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

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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

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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

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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