Network Coding-based Content Distribution in ...

IEEE ICC 2016

Network Coding-based Content Distribution in Cellular Access Networks Claudio Fiandrino

University of Luxembourg

Dzmitry Kliazovich Pascal Bouvry Albert Y. Zomaya

University of Sydney

May 24, 2016

Motivation

I

4.4 billion people will use mobile cloud applications by 2017

I

$ 46.90 billion market

I

Mobile cloud applications: 90% of all mobile data traffic by 2019 100%

50%

0

19 %

17 %

15 %

14 %

12 %

10 %

81 %

83 %

85 %

86 %

88 %

90 %

2014

2015

2016

2017

2018

2019

Non-Cloud Cloud

Figure: Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2014-2019

Claudio Fiandrino | IEEE ICC 2016 | Network Coding-based Content Distribution in Cellular Access Networks

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Mobile data traffic

30 EB

30.6 EB

Mobile Networks 2014

Mobile Networks 2020 (per month)

1 EB Global Internet 2000

Figure: Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2015-2020

Claudio Fiandrino | IEEE ICC 2016 | Network Coding-based Content Distribution in Cellular Access Networks

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The key idea

Objective Optimizing information delivery of flows with overlapping or partially overlapping content Network Coding

I

Geographically co-located users

I

Mobile cloud applications content I I I

Location Based Services Maps Meteo

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Scenario I

vNC-CELL in Mobile Cloud I

Virtualization of network coding operations

Buffer

Network Coding

S-GW eNodeB

Ue

Mobile Cloud

P-GW MME

E-UTRAN

Cloud

Mobile Operator Network I P-GW: Packet Data Network Gateway I S-GW: Serving Gateway I MME: Mobility Management Entity I E-UTRAN: Evolved-Universal Terrestrial Radio Access Network Claudio Fiandrino | IEEE ICC 2016 | Network Coding-based Content Distribution in Cellular Access Networks

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vNC-CELL key aspects

I

Monitor and cache in transit traffic

I

Identify coding opportunities

Coding Opportunities Information needed by two or more users delivered with a single coded transmission

I

XOR to combine packets

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Example U E -1

U E -2

M OBILE C LOUD

Request content A

A PPLICATION

Packet request Packet AU E -1

Send content A

Cache and forward AU E -1 Packet AU E -1 Process and store AU E -1 Request content B

Packet request Packet BU E -2

Packet BU E -2

Send content BU E -2

Cache and forward BU E -2

Process and store BU E -2 Request content B

Packet request Packet BU E -1

Send content BU E -1

Check if B is in buffer Packet (A ⊕ B)U E -1,U E -2 Decode B using AU E -1

Coding (A ⊕ B)U E -1,U E -2

Decode A using BU E -2

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Example U E -1

U E -2

M OBILE C LOUD

Request content A

A PPLICATION

Packet request Packet AU E -1

Send content A

Cache and forward AU E -1 Packet AU E -1 Process and store AU E -1 Request content B

Packet request Packet BU E -2

Packet BU E -2

Send content BU E -2

Cache and forward BU E -2

Process and store BU E -2 Request content B

Packet request Packet BU E -1

Send content BU E -1

Check if B is in buffer Packet (A ⊕ B)U E -1,U E -2 Decode B using AU E -1

Coding (A ⊕ B)U E -1,U E -2

Decode A using BU E -2

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Example U E -1

U E -2

M OBILE C LOUD

Request content A

A PPLICATION

Packet request Packet AU E -1

Send content A

Cache and forward AU E -1 Packet AU E -1 Process and store AU E -1 Request content B

Packet request Packet BU E -2

Packet BU E -2

Send content BU E -2

Cache and forward BU E -2

Process and store BU E -2 Request content B

Packet request Packet BU E -1

Send content BU E -1

Check if B is in buffer Packet (A ⊕ B)U E -1,U E -2 Decode B using AU E -1

Coding (A ⊕ B)U E -1,U E -2

Decode A using BU E -2

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Content distribution

cn−k,1

cn−k+1,2

⋮ ⋮

⋮ cn−1,k−1 ⊕ cn,k

cn−k,1 ⊕ cn−k+1,2

cn,k

Individual Transmission -

⋱

⋮ c2k−1,k−1 ⊕ c2k,k

ck+1,1 ⊕ ck+2,2

ck+2,2

ck+1,1

⋱

⋮ ck−1,k−1 ⊕ ck,k

ck,k c1,1

c2,2

u2 u1

c1,1 ⊕ c2,2

⋮

⋱

Users

uk

c2k,k

Optimal allocation for content distribution

Encoded Transmission

Claudio Fiandrino | IEEE ICC 2016 | Network Coding-based Content Distribution in Cellular Access Networks

t

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Setting

I

NS-3 Simulations

I

Num. users: 2-10

I

Chunk request rate: uniformly distributed between 100 and 200ms

I

Chunk size: 50 Bytes

I

UDP transmissions IP Header UDP Header Chunk Header Chunk Payload ChunkId EncIdA EncIdB

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Evaluation Metrics

I

Number of transmissions performed by eNodeB

I

Individual and encoded transmissions

I

Number of transmissions received by mobile users

I

Download time comparison

I

Distribution of transmissions in presence of channel errors

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Number of transmissions performed by eNodeB

Num. transmissions

600 500

vNC-CELL Individual vNC-CELL Encoded No vNC-CELL

400 300 200 100 0

I

2

4

6 Num. users

8

10

Individual transmissions remains almost constant

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100 50 0 0

2.5

5

Simulation time (s)

I

7.5

10 2

4

6

8

10

Num. users

Num. transmissions

Num. transmissions

Individual and encoded transmissions

200

100

0 0

2.5

5

7.5

10 2

4

Simulation time (s)

6

8

10

Num. users

Encoded transmission higher when buffer in mobile cloud fills up

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Number of transmissions received by mobile users 160

Num. transmissions

140

Content packets vNC-CELL Individual vNC-CELL Encoded

120 100 80 60 40 20 0

I

2

4

6 Num. users

8

10

Users receive more encoded packets than needed for the content I I

Increase in reliability Increase in cost for decoding

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Download time comparison 80

Download time (s)

vNC-CELL

NO vNC-CELL

60

40

20

0

0

100

200 300 Num. chunks

400

500

I

4 users download 500 chunks randomly from a 10 000-chunk file

I

vNC-CELL still achieves approximately 10% shorter download times

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100 50 0 100

200

300

400

500

Distance from eNodeB (m)

2

4

6

8

Num. users

Content

10

Num. transmissions

Num. transmissions

Num. transmissions

Distribution of transmissions with channel errors

100 50 0 100

200

300

400

500

2

Distance from eNodeB (m)

4

6

8

Num. users

10

100 50 0 100

COST-Hata model

I

vNC-CELL is scalable and resilient to errors

300

400

500

2

4

Distance from eNodeB (m)

Individual

I

200

Claudio Fiandrino | IEEE ICC 2016 | Network Coding-based Content Distribution in Cellular Access Networks

6

10

8

Num. users

Encoded

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Conclusion

Summary I

Efficient content distribution for cloud applications

I

Network coding and caching performed at mobile cloud

Take home message I

Download time gain

I

Scalable and resilient to errors

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Thank You! Claudio Fiandrino