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Augmenting Mobile 3G Using WiFi Sam Baek Ran Li Modified from University of Massachusetts Microsoft Research.

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Presentation on theme: "Augmenting Mobile 3G Using WiFi Sam Baek Ran Li Modified from University of Massachusetts Microsoft Research."— Presentation transcript:

1 Augmenting Mobile 3G Using WiFi Sam Baek Ran Li Modified from University of Massachusetts Microsoft Research

2 Outline The necessity of augmenting 3G Basic idea of Wiffler Improvement of Wiffler and test results Questions 2

3 Demand for mobile access growing 3 Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2011–2016 global mobile data traffic will increase 18-fold between 2011 and 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.

4 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

5 Augmenting Mobile 3G using WiFi Offload data to WiFi when possible Easy to do when you are stationary Focus on vehicular mobility 5

6 Offloading 3G data to WiFi 6 Wiffler

7 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) 7

8 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: hours of data over 12+ days

9 3G and WiFi access availability 9 Availability (%) 3G+WiFi combination is better than 3G

10 Special distribution of 3G/WiFi availability 10 Amherst

11 WiFi (802.11b) throughput is lower 11 Cumulative fraction WiFi 3G WiFi 3G Upstream Downstream Throughput = Total data received per second

12 WiFi loss rate is higher 12 Cumulative fraction WiFi 3G 28% 8% Loss rate = Fraction of packets lost at 10 probes/sec

13 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. 13

14 14 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)

15 15 Key ideas in Wiffler Increase 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

16 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. 16 Predicted WiFi transfer size in next D seconds

17 17 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

18 18 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

19 Wiffler implementation 19 Wiffler proxy  Prediction-based offloading upstream + downstream  Fast switching only upstream  Implemented using signal-upon-ACK in driver

20 20 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

21 Deployment results Data offloaded to WiFi Wiffler’s prediction-based offloading 30% WiFi when available10% 21 % time good voice quality Wiffler’s fast switching68% 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

22 22 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

23 Wiffler increases data offloaded to WiFi 23 Workload: Web traces obtained from commuters Wiffler increases delay by 10 seconds over Oracle. 42% 14% Wiffler close to Oracle Sophisticated prediction yields negligible benefit WiFi when available yields little savings

24 Even more savings in urban centers 24

25 Fast switching improves quality of delay-sensitive applications 25 40% 58% 73% 30% data offloaded to WiFi with 40ms switching threshold

26 26 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

27 27 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?

28

29 Demand projected to outstrip capacity 29

30 Error in predicting # of APs 30 Relative error N=1 N=4 N=8

31 Fast switching improves performance of demanding applications 31 % time with good voice quality Oracle Only 3G Wiffler No switching


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