Internet Service in Developing Regions
Through Network Coding
Mike P. Wittie, Kevin C. Almeroth, Elizabeth M. Belding,
Department of Computer Science
UC Santa Barbara
Ivica Rimac, Volker Hilt
Bell Labs
Alcatel-Lucent
Slide 1 of 16
Networking and the Digital Divide
•
The Digital Divide
– Low penetration of Internet services
– Higher price
– Lack of adequate infrastructure
•
Success of cellular deployments
– No data services
– High subscription price
•
Rural mesh networks
– Local communication patterns
Broadband price (USD/month)
400
350
300
250
200
150
100
50
•
Goals:
– Low cost data communications
– Leverage cellular deployments
– Cater to local communications
0
US
Europe
India
Sub-saharan
Africa
Slide 2 of 16
Multihop Cellular Networks (MCNs)
•
Cellular network augmented by
client-to-client Wi-Fi
communications [Lin00] (A)
•
Rural (sparse) MCNs
– Large cell area
– Large per-client spectrum usage
•
A.
B.
Local traffic patterns (B):
– Cannot use cell tower
– Cannot form end-to-end paths
•
Need: efficient opportunistic
client-to-client forwarding in
sparse MCNs
Slide 3 of 16
Delay Tolerant Networks (DTNs)
•
Epidemic Routing [Vahdat00]
– Bundled data forwarded during every
contact for eventual delivery
– Flood scoping by hop-count or TTL
•
S
PRoPHET [Lindgren04]
– Transitive destination contact
probability as routing metric
– Data forwarded up a routing metric
gradient
•
D
•
Cloud Routing (CR) [Wittie09]
– Network and traffic state
disseminated over a control channel
– Forwards a small set of data copies
– Lower forwarding cost and higher
network throughput
But, high cost of flooding
creates network congestion
•
But, replication wastes network
resources
Slide 4 of 16
Intra-flow Network Coding (NC)
• Forwards randomly
encoded data on each
path
• With high probability, data
arriving on multiple paths
is innovative
• Codes are embedded in
packets themselves
[Chou03]
𝒗1 𝐸1 | 𝒗1 𝐷1
𝒗2 𝐸2 | 𝒗1 𝐷1
⋮ | ⋮
𝒗3 𝐸3 | 𝒗1 𝐷1
𝑛 bytes of data
𝐷𝑝×𝑛/𝑝 data matrix
D
𝐸𝑝×𝑝 encoding matrix, initially 𝐼
𝒗1×𝑝 , 𝒗𝑖S ∈ 𝒢ℱ(8)
Coded piece:
𝐺𝑎𝑢𝑠𝑠𝑖𝑎𝑛 𝑒𝑙𝑖𝑚 .
𝒗𝐸 | 𝒗𝐷
𝐼
|
|
𝐷
|
|
1×(𝑝+𝑛/𝑝)
𝑝×(𝑝+𝑛/𝑝)
Slide 5 of 16
NC in DTNs
• Network Coding
Probabilistic Routing
(NCPR) [Widmer05]
– Each node forwards floor(d)
coded pieces and additional
coded piece with probability
d-floor(d)
– Stops forwarding after
ceil(d) coded pieces
– New innovative coded
pieces reset forwarding cap
D
S
• But, tradeoff between
high delivery rates and
high load
• Need a more efficient
mechanism for reliability
Slide 6 of 16
Semi-Innovative Set Routing (SISR)
 SISR (scissor) forwards:
- small forwarding footprint (CR)
- fraction of data on each path
through NC (NCPR)
 Coded pieces required to
decode bundle: 𝑏 = 𝑝
 Redundant coded pieces: 𝑟 =
D
S
𝑏
4
 Maximum coded pieces at node
(bundle fraction): 𝑓 = 𝑟
b–f b
f
r
Linearly independent?
Slide 7 of 16
Semi-Innovative Sets (SISs)
 SIS 𝑠 is a set of linearly independent
coded pieces
 Given a set of coded pieces 𝐶,
𝐶 = 𝑏 + 𝑟, 𝑟 ≥ 1, we can construct a
set of SISs over 𝐶, such that any subset
of 𝐶 of size 𝑏 has full rank.
D
S
b–f b
𝐶
𝑠1 , 𝑠2 , ⋯ , 𝑠
s1
|𝐶|
𝑏/2
every possible union of 𝑠𝑖 , 𝑠𝑗
r
f
s2
f
s3
SIS1
SIS2
SIS3
Slide 8 of 16
Semi-Innovative Sets (SISs)
 SISs can be constructed to tolerate any
number of losses 𝑙 = 𝑟/𝑓 , 𝑓 ≤ 𝑟
D
S
𝑙 = 3 → 𝐶 = 2𝑏, 𝑓 =
1
3
every possible union of 𝑠𝑖 , 𝑠𝑗
f
b b –bf
f
r-2f
r
rf
SIS1
SIS2
SIS3
SIS4
SIS5
SIS6
Slide 9 of 16
SISR in an MCN
 Embedded codes disseminated
over the control channel to
announce forwarding progress
D
 While the number of SISs grows
𝑏+𝑟
exponentially as
𝑏/2
, each
𝑏/2
− 1 SISs
 When 𝑑 coded pieces are
delivered, the global encoding
adjusts accordingly
n2
S
2
node only needs to maintain
𝑏+𝑟
𝑥, 𝑦 , 𝒗𝐸
n1
b b
n3
r
r
d
SIS1
SIS2
SIS3
SIS4
SIS5
SIS6
Slide 10 of 16
SISR Cloud Progress Example
Slide 11 of 16
Evaluation Setup
• Want to compare SISR with CR and NCPR
– NCPR – flooding and network coding
– CR – small set of bundle copies
– SISR – network coded bundle + redundancy
• Configuration details:
– Area, node density and mobility models a rural community
– Single flow between a node pair at different distances
– Interested in evaluating:
• Bundle forwarding cost
• End-to-end delay
• Control channel load
Slide 12 of 16
Forwarding Cost
• Forwarding cost
– the amount of data forwarded
in the network before delivery
• NCPR – high cost of flooding
• CR – high cost of replication
• SISR – lowest cost
– Fraction of data on each path
– Improvements for multiple
simultaneous flows
Slide 13 of 16
Overhead of Control Traffic
• Control channel load
– Position updates
– Bundle progress notifications
– Data encoding vectors (SISR
only)
• Cellular channel gain
– Bundle size minus control traffic
• Prevalence of position updates
• Higher gain for multiple flows
• Gain higher for CR, but SISR
easier on client resources
Slide 14 of 16
Conclusions and Future Work
•
•
Introduced Semi-Innovative Set
Routing (SISR)
•
End-to-end management of NC
and forwarding mechanisms
2X reduction in forwarding cost
– Lower cost of infrastructure and data
services
– Make data services affordable for
more clients
D
– Adaptation to different network
settings
– Directional mesh networks with smart
antennas
S
– Different
control
A. ratios of data and
B.
traffic propagation speeds
– Only innovative data forwarded
– Tolerates any number of losses
•
Future work:
b
r
SIS1
SIS2
SIS3
SIS4
SIS5
SIS6
Slide 15 of 16
Thank You
Mike Wittie
mwittie@cs.ucsb.edu
Slide 16 of 16
Q&A
Slide 17 of 16
Slide 18 of 16
Backup
Slides
Slide 19 of 16
Evaluation Setup
• Want to compare SISR
with CR and NCPR
• Configuration details:
– Area, node density and
mobility models a rural
community
– Single flow between
random node pair
– NCPR – d configured for
100% delivery at 6km
– CR – lower forwarding cost
at delay comparable to
larger clouds
– SISR – lowest delay at 6km
Slide 20 of 16
Bundle Delay
• Delay
– end-to-end forwarding delay
of entire bundle
(all coded pieces)
• SISR - last copy delay
• NCPR – nodes use up
forwarding allowance
before delivery
• CR – first copy delay
Slide 21 of 16
Multihop Cellular Networks (MCNs)
• Cellular network augmented
by client-to-client Wi-Fi
communications [Lin00]
• MCNs can:
– Reduce cellular channel load
(A)
– Extend cell coverage (B, C)
• MCNs make cellular
infrastructure go further
Slide 22 of 16
MCNs in Developing Regions
• Sparse MCNs
– Fewer clients and larger cell area
– Larger per-client spectrum usage
• Local data communications
– Regional caches (B)
– Opportunistic client-to-client
communications (C)
• Our focus: opportunistic clientto-client forwarding in sparse
MCNs
Slide 23 of 16