network coding-based content distribution in cellular ...€¦ · the key idea objective ... i mme:...
TRANSCRIPT
IEEE ICC 2016
Network Coding-based Content Distribution inCellular Access Networks
Claudio Fiandrino
Dzmitry Kliazovich
Pascal Bouvry
University of Luxembourg
Albert Y. Zomaya University of Sydney
May 24, 2016
Motivation
I 4.4 billion people will use mobile cloud applications by 2017I $ 46.90 billion marketI Mobile cloud applications: 90% of all mobile data traffic by 2019
2014 2015 2016 2017 2018 20190
50%
100%19% 17% 15% 14% 12% 10%
81% 83% 85% 86% 88% 90%
Non-CloudCloud
Figure: Cisco Visual Networking Index: Global Mobile Data Traffic ForecastUpdate, 2014-2019
Claudio Fiandrino | IEEE ICC 2016 | Network Coding-based Content Distribution in Cellular Access Networks 1 of 15
Mobile data traffic
1 EB
30 EB 30.6 EB
Global Internet2000
Mobile Networks2014
Mobile Networks2020 (per month)
Figure: Cisco Visual Networking Index: Global Mobile Data Traffic ForecastUpdate, 2015-2020
Claudio Fiandrino | IEEE ICC 2016 | Network Coding-based Content Distribution in Cellular Access Networks 2 of 15
The key idea
Objective
Optimizing information delivery of flows with overlapping or partiallyoverlapping content Network Coding
I Geographically co-located users
I Mobile cloud applications contentI Location Based ServicesI MapsI Meteo
Claudio Fiandrino | IEEE ICC 2016 | Network Coding-based Content Distribution in Cellular Access Networks 3 of 15
Scenario
I vNC-CELL in Mobile CloudI Virtualization of network coding operations
Mobile Operator Network
E-UTRAN Cloud
P-GW
MME
S-GW
eNodeBMobile Cloud
Ue
Buffer Network Coding
I P-GW: Packet Data Network GatewayI S-GW: Serving GatewayI MME: Mobility Management EntityI E-UTRAN: Evolved-Universal Terrestrial Radio Access Network
Claudio Fiandrino | IEEE ICC 2016 | Network Coding-based Content Distribution in Cellular Access Networks 4 of 15
vNC-CELL key aspects
I Monitor and cache in transit trafficI Identify coding opportunities
Coding Opportunities
Information needed by two or more users delivered with asingle coded transmission
I XOR to combine packets
Claudio Fiandrino | IEEE ICC 2016 | Network Coding-based Content Distribution in Cellular Access Networks 5 of 15
Example
UE-1 UE-2 MOBILE CLOUD APPLICATION
Request content A Packet request
Send content APacket AUE-1
Cache and forward AUE-1Packet AUE-1
Process and store AUE-1Request content B
Packet request
Send content BUE-2Packet BUE-2
Cache and forward BUE-2Packet BUE-2
Process and store BUE-2Request content B Packet request
Send content BUE-1Packet BUE-1
Check if B is in buffer
Coding (A ⊕ B)UE-1,UE-2Packet (A ⊕ B)UE-1,UE-2
Decode A using BUE-2Decode B using AUE-1
Claudio Fiandrino | IEEE ICC 2016 | Network Coding-based Content Distribution in Cellular Access Networks 6 of 15
Example
UE-1 UE-2 MOBILE CLOUD APPLICATION
Request content A Packet request
Send content APacket AUE-1
Cache and forward AUE-1Packet AUE-1
Process and store AUE-1Request content B
Packet request
Send content BUE-2Packet BUE-2
Cache and forward BUE-2Packet BUE-2
Process and store BUE-2Request content B Packet request
Send content BUE-1Packet BUE-1
Check if B is in buffer
Coding (A ⊕ B)UE-1,UE-2Packet (A ⊕ B)UE-1,UE-2
Decode A using BUE-2Decode B using AUE-1
Claudio Fiandrino | IEEE ICC 2016 | Network Coding-based Content Distribution in Cellular Access Networks 6 of 15
Example
UE-1 UE-2 MOBILE CLOUD APPLICATION
Request content A Packet request
Send content APacket AUE-1
Cache and forward AUE-1Packet AUE-1
Process and store AUE-1Request content B
Packet request
Send content BUE-2Packet BUE-2
Cache and forward BUE-2Packet BUE-2
Process and store BUE-2Request content B Packet request
Send content BUE-1Packet BUE-1
Check if B is in buffer
Coding (A ⊕ B)UE-1,UE-2Packet (A ⊕ B)UE-1,UE-2
Decode A using BUE-2Decode B using AUE-1
Claudio Fiandrino | IEEE ICC 2016 | Network Coding-based Content Distribution in Cellular Access Networks 6 of 15
Content distribution
Optimal allocation for content distribution
t
u1
u2
⋮uk
Use
rs
c 1,1
c 2,2
⋱c k
,k
c k+1,1
c k+2,2
⋱c 2
k,k
⋮ ⋮c n−k,1
c n−k+
1,2
⋱c n
,k
c 1,1⊕c 2
,2 ⋮c k−1,k−1⊕
c k,k
c k+1,1
⊕c k+2,2 ⋮
c 2k−1
,k−1⊕
c 2k,
k
c n−k,1
⊕c n−k+
1,2
⋮c n−1,k−1⊕
c n,k
Individual Transmission Encoded Transmission-
Claudio Fiandrino | IEEE ICC 2016 | Network Coding-based Content Distribution in Cellular Access Networks 7 of 15
Setting
I NS-3 SimulationsI Num. users: 2-10I Chunk request rate: uniformly distributed between 100 and 200msI Chunk size: 50 BytesI UDP transmissions
IP Header UDP Header Chunk Header Chunk Payload
EncIdAChunkId EncIdB
Claudio Fiandrino | IEEE ICC 2016 | Network Coding-based Content Distribution in Cellular Access Networks 8 of 15
Evaluation Metrics
I Number of transmissions performed by eNodeBI Individual and encoded transmissionsI Number of transmissions received by mobile usersI Download time comparisonI Distribution of transmissions in presence of channel errors
Claudio Fiandrino | IEEE ICC 2016 | Network Coding-based Content Distribution in Cellular Access Networks 9 of 15
Number of transmissions performed by eNodeB
2 4 6 8 100
100
200
300
400
500
600
Num. users
Num
.tra
nsm
issi
ons
vNC-CELL IndividualvNC-CELL EncodedNo vNC-CELL
I Individual transmissions remains almost constant
Claudio Fiandrino | IEEE ICC 2016 | Network Coding-based Content Distribution in Cellular Access Networks 10 of 15
Individual and encoded transmissions
0 2.5 5 7.5 10 24
68
100
50
100
Simulation time (s)Num. users
Num
.tra
nsm
issi
ons
0 2.5 5 7.5 10 24
68
100
100
200
Simulation time (s)Num. users
Num
.tra
nsm
issi
ons
I Encoded transmission higher when buffer in mobile cloud fills up
Claudio Fiandrino | IEEE ICC 2016 | Network Coding-based Content Distribution in Cellular Access Networks 11 of 15
Number of transmissions received by mobile users
2 4 6 8 100
20
40
60
80
100
120
140
160
Num. users
Num
.tra
nsm
issi
ons
Content packetsvNC-CELL IndividualvNC-CELL Encoded
I Users receive more encoded packets than needed for the contentI Increase in reliabilityI Increase in cost for decoding
Claudio Fiandrino | IEEE ICC 2016 | Network Coding-based Content Distribution in Cellular Access Networks 12 of 15
Download time comparison
0 100 200 300 400 5000
20
40
60
80
Num. chunks
Dow
nloa
dtim
e(s
)
vNC-CELL NO vNC-CELL
I 4 users download 500 chunks randomly from a 10 000-chunk fileI vNC-CELL still achieves approximately 10% shorter download times
Claudio Fiandrino | IEEE ICC 2016 | Network Coding-based Content Distribution in Cellular Access Networks 13 of 15
Distribution of transmissions with channel errors
100200
300400
500 24
68
100
50
100
Distance from eNodeB (m)Num. users
Num
.tra
nsm
issi
ons
100200
300400
500 24
68
100
50
100
Distance from eNodeB (m)Num. users
Num
.tra
nsm
issi
ons
100200
300400
500 24
68
100
50
100
Distance from eNodeB (m)Num. users
Num
.tra
nsm
issi
ons
Content Individual Encoded
I COST-Hata modelI vNC-CELL is scalable and resilient to errors
Claudio Fiandrino | IEEE ICC 2016 | Network Coding-based Content Distribution in Cellular Access Networks 14 of 15
Conclusion
Summary
I Efficient content distribution for cloud applicationsI Network coding and caching performed at mobile cloud
Take home message
I Download time gainI Scalable and resilient to errors
Claudio Fiandrino | IEEE ICC 2016 | Network Coding-based Content Distribution in Cellular Access Networks 15 of 15
