When David helps Goliath: The case for 3G Onloading · 3G throughput is highly variable !...

When David helps Goliath: The case for 3G Onloading Narseo Vallina-Rodriguez, Vijay Erramilli, Yan Grunenberger,

Laszlo Gyarmati, Nikos Laoutaris, Rade Stanojevic,

Dina Pagagiannaki

University of Cambridge

Telefonica Research / Telefonica Digital

HotNets 2012, Redmond, WA

David and Goliath

100x

47 Mbps downlink 5.6 Mbps uplink

Cellular Network

200m

Wired Network (DSL)

5.8 Gbps downlink 2.3 Gbps uplink

2

Narseo Vallina-Rodriguez, University of Cambridge/Telefonica Research, HotNets'12

Can David help Goliath?

YES! ¤  For 1 ADSLà the entire BS is not David

¤  For all ADSLs à David can become Goliath FOR SHORT PERIODS OF TIME

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3G Onloading

¤ Offload traffic from the wired network onto the cellular network ¤  Speed up wired connections ¤  Improve applications’ performance

¤  Limitations: ¤  3GOL cannot assist all wired connections ¤  Cannot help applications at all times

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Scenario 1: David helps Goliath

A

3.4 Mbps

4.7 Mbps

¤ Video-streaming application

¤ Well provisioned area

¤  Spare capacity on cellular network ¤  Powerboost ¤  Use-and-release

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Scenario 2: David becomes Goliath

2 Km 2.8 Mbps

A

¤ Constrained wired networks

¤ Cellular network can provide more capacity than wired networks

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4.7 Mbps


Design Considerations

¤  The Network ¤  3G throughput is highly variable ¤  Control-plane latency

¤  The User Economics: volume caps in data plans ¤  40 % of the mobile users use less than 10% of their

cap

¤ The Service ¤  Network integrated service ¤  Over the top

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Preliminary results

¤ How much additional throughput?

¤  Performance improvement?

¤  Increase in cellular traffic?

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How much additional throughput?

¤  The Network ¤  3G throughput depends on a number of factors ¤  Limited 3G network backhaul capacity ¤  Signaling delay

¤  The Economics ¤  Data volume caps

¤  The Service ¤  Network integrated service ¤  Over the top

Scenario Time DSL (d/u) Mbps

1. Densely populated residential area (city center)

1 am 3.44 / 0.30

2. Office area rush hour 4 pm 4.51 / 0.47

3. Residential Area in tourist hotspot

10 pm 6.72 / 0.84

4. Sparsely populated residential area (suburbs)

1 am 2.84 / 0.45

5. Popular shopping center in peak time

2 pm n/a

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Upstream Downstream

0

5000

10000

15000

1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10Cluster Size

Agg

rega

ted

Thro

ughp

ut (K

bps)

location Location1 Location2 Location3 Location4 Location5


Upstream Downstream

0

5000

10000

15000

1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10Cluster Size

Agg

rega

ted

Thro

ughp

ut (K

bps)

location Location1 Location2 Location3 Location4 Location5Densely populated area

Upstream Downstream

0

5000

10000

15000

1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10Cluster Size

Agg

rega

ted

Thro

ughp

ut (K

bps)

location Location1 Location2 Location3 Location4 Location5Office area

Upstream Downstream

0

5000

10000

15000

1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10Cluster Size

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rega

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bps)

location Location1 Location2 Location3 Location4 Location5Residential area. Tourist hotspot

Upstream Downstream

0

5000

10000

15000

1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10Cluster Size

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rega

ted

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ughp

ut (K

bps)

location Location1 Location2 Location3 Location4 Location5Suburbs

Upstream Downstream

0

5000

10000

15000

1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10Cluster Size

Agg

rega

ted

Thro

ughp

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bps)

location Location1 Location2 Location3 Location4 Location5Shopping center (city center)

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Scenario Time DSL (d/u) Mbps

Cellular (d/u) Mbps

3GOL/DSL (d/u)

1. Densely populated residential area (city center)

1 am 3.44 / 0.30 4.37 / 1.64 2.3 / 6.5

2. Office area rush hour 4 pm 4.51 / 0.47

4.56 / 5.18 2.0 / 12.0

3. Residential Area in tourist hotspot

10 pm 6.72 / 0.84 1.92 / 1.53 1.3 / 2.8

4. Sparsely populated residential area (suburbs)

1 am 2.84 / 0.45 4.67 / 3.89 2.7 / 9.7

5. Popular shopping center in peak time

2 pm n/a 5.31 / 2.64

n/a

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Performance improvement: Video-streaming

0 5 10 15 200

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

dsl/3GOL

fract

ion

of v

ideo

s

Empirical CDF

1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.60

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

dsl/3GOl(40MB)fra

ctio

n of

use

rs

Empirical CDF

Unlimited Capped

50% of the videos have a speed up factor of 10 and below

40% of users have a speed up around 20%

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0 2 4 6 8 10 12 14 16 18 20 22 24

101

102

103

104

105

time (5min bins)

loa

d o

n n

wk

(Mb

s)

3GOL(20Mb budget)

3GOL(Unlim)

Cell capacity

Increase in 3G traffic

Available Bandwidth

Overload

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Prototype implementation

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¤ Decide when to use 3GOL ¤  Which apps and when!

¤  Use the right devices ¤  Cell Nets are best effort! ¤  Pre-fetching radio channel

¤ How much data to onload

¤ How to aggregate different connections: ¤  Same Wi-Fi AP ¤  ClubDSL [Mobicom 2011]

Research challenges

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Related Work

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¤  Wired networks performance. ¤  Sundaresan et al. Broadband internet performance: a view from the

gateway. ACM SIGCOMM 2011. ¤  Kreibich et al. NetAlyzr, IMC 2010

¤  3G Offloading ¤  Badam et al. The hare and the tortoise: taming wireless losses by exploiting

wired eliability. In MobiHoc, 2011. ¤  Lee et al. Mobile data offloading: how much can wifi deliver? CoNext 2010

¤  Mobile networks ¤  Ha et al. Tube. ACM Sigcomm, 2012 ¤  Huang et al. A close examination of performance and power

characteristics of 4G/LTE networks ¤  Qiang et al. Characterizing radio resource allocation for 3g networks ¤  Vallina-Rodriguez et al. Breaking for commercials (To appear) IMC 2012

Summary

¤ David can help Goliath ¤  Powerboost-like service ¤  Constrained wired networks

¤ Overhead in cellular network can be controlled ¤  Use and release ¤  Data caps

¤  Better performance expected with LTE deployment

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Questions?

Narseo Vallina-Rodriguez [email protected] University of Cambridge / Telefonica Research

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When David helps Goliath: The case for 3G Onloading · 3G throughput is highly variable !...

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