by
Aruna Balasubramanian
Ratul Mahajan
Arun Venkataramani
University of Massachusetts
Microsoft Research
Presented by
Ashok Kumar J
CS 752/852 - Wireless and Mobile Networking

www.totaltele.com
http://www.readwriteweb.com
900 million mobile broadband subscriptions today….
www.3gamericas.org
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Mobile demand is projected to far exceed capacity www.nytimes.com
Current spectrum www.rysavy.com
409.5 MHz
230 MHz Unallocated spectrum
(including whitespaces)
Projected demand by
2016
800 MHz –
1000 MHz www.nytimes.com
“In light of the limited natural resource of spectrum, we have to look at the ways of conserving spectrum” -- Mark Siegel (AT&T)
Reducing cellular spectrum utilization is key!
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Demand projected to outstrip capacity http://www.nytimes.com
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How can we reduce spectrum usage?
blogs.chron.com
1. Behavioral www.usatoday.com
2. Economic
3. Technical
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Offload data to WiFi when possible
Focus on vehicular mobility
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 Improving performance using handoffs based on current conditions
 Reducing power consumption by switching across multiple interfaces
This work:
1.How much 3G data can be offloaded to WiFi?
2.How to offload without hurting applications?
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 Measurement: Joint study of 3G and WiFi connectivity

Across three cities: Amherst, Seattle, SFO
 System: Wiffler, to offload 3G data to WiFi while respecting application constraints

Deployed on 20 vehicles
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 Testbed: Vehicles with 3G and WiFi (802.11b) radios

Amherst: 20 buses + 1 car, Seattle: 1 car, SFO: 1 car
 Software: Simultaneously probes 3G and WiFi for

Availability, loss rate, throughput
 Duration: 3000+ hours of data over 12+ days
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Open WiFi availability low, but useful
Availability = fraction of 1-second intervals when at least one packet received
86%
Availability
(%)
3G+WiFi combination better than sum pf parts
11%
7%
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Loss rate = Fraction of packets lost at 10 probes/sec
Cumulative fraction
28%
8% 3G
WiFi
Wi-Fi loses are bursty.
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Throughput = Total data received per second
Cumulative fraction
3G
0.35 0.72
WiFi
Upstream
Cumulative fraction
3G
WiFi
Downstream
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 Availability of 3G in Peak hours is less compared to its availability in non-peak hours.
 Availability of Wi-Fi in Peak hours is more compared to its availability in off-Peak hours.
 Unavailability of 3G is 11% but when combined with Wi-Fi, total unavailability reduced to 5%.
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 In 47% of the Locations, Data sent over Wi-Fi is insignificant compared to 3G.
 In remaining 53% of locations, at least 20% of the 3G data could be shifted to Wi-Fi.
 In 9% of the locations, equal or more data sent over Wi-Fi compared 3G. So in these location entire traffic could be offloaded to Wi-Fi.
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 Strawman augmentation: Use Wi-Fi when available

Can offload only ~11% of the time

Can hurt applications performance because of Wi-
Fi's higher loss rate and lower throughput

Example: Applications sensitive to losses like VoIP and Video Conferencing will face degraded application quality while transmitting over Wi-Fi but Application like Email, sending a file wouldn’t.
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Leveraging Delay Tolerance:
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
Fast Switching to 3G:
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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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 Wi-Fi.
Predicted WiFi transfer size in next D seconds
Choosing a delay threshold involves a trade off between better application performance and 3G load.
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 History-based prediction of # of Aps encounter until a future time using average inter-meeting time of the past encounters. (T/I encounter in next T secs where I is avg)
 Similarly average throughput is estimated based on the throughput observed by each vehicle at each AP encounter.

WiFi capacity = (expected #APs) x (capacity per AP)
Negligible benefits with more sophisticated prediction, eg future location prediction + AP location database
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Relative error
N=1
N=4
N=8
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 Accuracy is Low when predicting with only one previous encounter.
 Predicting error is close to 20% even for predicting Ap encounter untill small future time interval of 20 secs.
 When prediction is based on the previous 4 or 8 AP encounters, the predictions error is less than 5% up to a future prediction time-interval of 50 seconds.
 The prediction error increases to 20% for prediction time interval of 100sec.
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 Problem:

WiFi losses bursty => high retransmission delay
 Approach:

If no WiFi link-layer ACK within 50ms, switch to 3G

Else, continue sending on WiFi
 Wi-Fi NIC frequently takes a long time to complete retransmission attempts. Madwifi which used in test beds retries packets 11 times, which even if we ignore medium access delays takes more than 120 milliseconds with default 802.11b specification.
 So fast switching mechanism send the packet on 3G if the Wi-Fi link-layer fails to deliver the packet within a delay threshold.
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 Wiffler needs to know the delay tolerance threshold and the QoS requirements of each application that uses the network.
 Wiffler requires proxy support, both to impliment fast switching and the predictionbased offloading. Proxy will fecilitate packet reception from multiple IP address (ie from the 3G and the Wifi interfaces) and allow switching between interfaces.
 Experiments are based open WiFi APs, Wiffler can be deployed used with other APs as well.
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Wiffler proxy
 Destination server also acts as proxy to manage data coming from different IP address that the client acquires as it moves.
 Prediction-based offloading upstream + downstream
 Delay threshold to 50 ms.
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Wiffler proxy
 Added a signal mechanism in the mobile node’s driver that signals the application when the wireless card receives a link layer acknowledgement.
 Fast switching only upstream

Fast switching in downstream direction is challenging because it needs either support from the APs or detailed information at the proxy on current Wi-Fi conditions(conveying the same is time consuming).
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 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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Wiffler’s prediction-based offloading
Data offloaded to WiFi
30%
WiFi when available 12%
File transfer size: 5MB; Delay tolerance: 60 secs;
Data generation 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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 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 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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 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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 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
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Questions?