Practical Network Coding for the Internet and Wireless Networks

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Practical Network Coding for the Internet and
Wireless Networks

• Philip A. Chouwith thanks to Yunnan Wu, Kamal
  Jain, Pablo Rodruiguez Rodriguez, Christos
  Gkantsidis, and Baochun Li, et al.
• Globecom Tutorial, December 3, 2004

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Outline

• Practical Network Coding
• Packetization
• Buffering
• Results for SprintLink ISP
• Experimental comparisons to multicast routing
• Internet and Wireless Network Applications
• Live Broadcasting, File Downloading, Messaging,
  Interactive Communication

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Network Coding applicable to real networks?

• Internet
• IP Layer
• Routers (e.g., ISP)
• Application Layer
• Infrastructure (e.g., CDN)
• Ad hoc (e.g., P2P)
• Wireless
• Mobile ad hoc multihop wireless networks
• Sensor networks
• Stationary wireless (residential) mesh networks

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Theory vs. Practice

• Theory
• Symbols flow synchronously throughout network
  edges have integral capacities
• Practice
• Information travels asynchronously in packets
  packets subject to random delays and losses edge
  capacities often unknown, varying as competing
  communication processes begin/end

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Theory vs. Practice (2)

• Theory
• Some centralized knowledge of topologyto compute
  capacity or coding functions
• Practice
• May be difficult to obtain centralized knowledge,
  or to arrange its reliable broadcast to nodes
  across the very communication network being
  established

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Theory vs. Practice (3)

• Theory
• Can design encoding to withstand failures,but
  decoders must know failure pattern
• Practice
• Difficult to communicate failure pattern
  reliablyto receivers

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Theory vs. Practice (4)

• Theory
• Cyclic graphs present difficulties, e.g.,
  capacity only in limit of large blocklength
• Practice
• Cycles abound. If A ? B then B ? A.

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Need to address practical network coding in real
networks

• Packets subject to random loss and delay
• Edges have variable capacities due to congestion
  and other cross traffic
• Node link failures, additions, deletions are
  common (as in P2P, Ad hoc networks)
• Cycles are everywhere
• Broadcast capacity may be unknown
• No centralized knowledge of graph topology or
  encoder/decoder functions
• Simple technology, applicable in practice

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Approach

• Packet Format
• Removes need for centralized knowledge of graph
  topology and encoding/decoding functions
• Buffer Model
• Allows asynchronous packets arrivals departures
  with arbitrarily varying rates, delay, loss
• Chou, Wu, and Jain, Allerton 2003
• Ho, Koetter, Médard, Karger, and Effros, ISIT
  2003

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Standard Framework

• Graph (V,E) having unit capacity edges
• Sender s in V, set of receivers Tt, in V
• Broadcast capacity h mint Maxflow(s,t)
• y(e) ?e be(e) y(e)
• b(e) be(e)e is local encoding vector

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Global Encoding Vectors

• By induction y(e) ?hi1 gi(e) xi
• g(e) g1(e),,gh(e) is global encoding vector
• Receiver t can recover x1,,xh from

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Invertibility of Gt

• Gt will be invertible with high probabilityif
  local encoding vectors are randomand field size
  is sufficiently large
• If field size 216 and E 28then Gt will be
  invertible w.p. 1-2-8 0.996
• Ho et al., 2003
• Jaggi, Sanders, et al., 2003

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Packetization

• Internet MTU size typically 1400 bytes
• y(e) ?e be(e) y(e) ?hi1 gi(e) xi s.t.

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Key Idea

• Include within each packet on edge eg(e) ?e
  be(e) g(e) y(e) ?e be(e) y(e)
• Can be accomplished by prefixing i th unit vector
  to i th source vector xi, i1,,h
• Then global encoding vectors needed to invert the
  code at any receiver can be found in the received
  packets themselves!

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Cost vs. Benefit

• Cost
• Overhead of transmitting h extra symbolsper
  packet if h 50 and field size 28,then
  overhead 50/1400 3
• Benefit
• Receivers can decode even if
• Network topology encoding functions unknown
• Nodes edges added removed in ad hoc way
• Packet loss, node link failures w/ unknown
  locations
• Local encoding vectors are time-varying random

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Erasure Protection

• Removals, failures, losses, poor random encoding
  may reduce capacity below h
• Basic form of erasure protectionsend redundant
  packets, e.g.,last h-k packets of x1,, xh are
  known zero

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Priority Encoding Transmission (Albanese et al.,
IEEE Trans IT 96)

• More sophisticated form partition data into
  layers of importance, vary redundancy by layer
• Received rank k ? recover k layers
• Exact capacity can be unknown

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Asynchronous Communication

• In real networks, unit capacity edges grouped
• Packets on real edges carried sequentially
• Separate edges ? separate prop queuing delays
• Number of packets per unit time on edge varies
• Loss, congestion, competing traffic, rounding
• Need to synchronize
• All packets related to same source vectors x1,,
  xh are in same generation h is generation size
• All packets in same generation tagged with same
  generation number one byte (mod 256) sufficient

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Buffering
edge
Transmission opportunity generate packet
random combination
arriving packets (jitter, loss, variable rate)
edge
edge
buffer
asynchronous transmission
asynchronous reception
edge
node
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Decoding

• Block decoding
• Collect h or more packets, hope to invert Gt
• Earliest decoding (recommended)
• Perform Gaussian elimination after each packet
• At every node, detect discard non-informative
  packets
• Gt tends to be lower triangular, so can typically
  decode x1,,xk with fewer more than k packets
• Much lower decoding delay than block decoding
• Approximately constant, independent of block
  length h

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Flushing Policy, Delay Spread,and Throughput loss

• Policy flush when first packet of next
  generation arrives on any edge
• Simple, robust, but leads to some throughput loss

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Interleaving

• Decomposes session into several concurrent
  interleaved sessions with lower sending rates
• Does not decrease overall sending rate
• Increases space between packets in each session
  decreases relative delay spread

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Simulations

• Implemented event-driven simulator in C
• Six ISP graphs from Rocketfuel project (UW)
• SprintLink 89 nodes, 972 bidirectional edges
• Edge capacities scaled to 1 Gbps / cost
• Edge latencies speed of light x distance
• Sender Seattle Receivers 20 arbitrary (5
  shown)
• Broadcast capacity 450 Mbps Max 833 Mbps
• Union of maxflows 89 nodes, 207 edges
• Send 20000 packets in each experiment, measure
• received rank, throughput, throughput loss,
  decoding delay vs.sendingRate(450),
  fieldSize(216), genSize(100), intLen(100)

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Received Rank
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Throughput
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Throughput Loss
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Decoding Delay
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Distributed Flow Algorithms

• Work in progress

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Comparing Network Codingto Multicast Routing

• Throughput
• Computational complexity
• Resource cost (efficiency)
• Robustness to link failure
• Robustness to packet loss

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Multicast Routing
sender
receivers

• Standard multicast union of shortest reverse
  paths
• Widest multicast maximum rate Steiner tree
• Multiple multicast Steiner tree packing

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Complexity and Throughput of Optimal Steiner Tree
Packing

• Packing Steiner trees optimally is hard e.g.,
  JMS03
• Network Coding has small throughput advantage
  LLJL04

Z. Li, B. Li, D. Jiang, and L. C. Lau, On
achieving optimal end-to-end throughput in data
networks theoretical and emprirical studies, ECE
Tech. Rpt., U. Toronto, Feb. 2004, reprinted with
permission.
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Complexity and Throughput of Optimal Steiner Tree
Packing (2)

• Z. Li, B. Li, D. Jiang, L. C. Lau 04
• Optimally packed Steiner trees in 1000 randomly
  generated networks (Elt35)
• Network Coding advantage 1.0 in every network
• The fundamental benefit of network coding is not
  higher optimal throughput, but to facilitate
  significantly more efficient computation and
  implementation of strategies to achieve such
  optimal throughput.

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Complexity ofGreedy Tree Packing

• Find widest (max rate) distribution tree in
  polynomial time Prim
• Grow tree edge by edge
• Postprocess tree
• Repeatedly pack widest distribution trees

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Complexity ofGreedy Tree Packing (2)

• Alternative method
• Break into unit-capacity edges between nodes
• Grow tree edge by edge
• Based on Lovaszconstructive proofof Edmonds
  Theorem
• Repeatedly pack distribution trees

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Wu, Chou, and Jain, ISIT 2004
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Comparison of Efficiency(receivers throughput
/ bandwidth cost)
Network coding
Widest tree
Z. Li, B. Li, D. Jiang, and L. C. Lau, On
achieving optimal end-to-end throughput in data
networks theoretical and emprirical studies, ECE
Tech. Rpt., U. Toronto, Feb. 2004, reprinted with
permission.
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Robustness to Link Failure

• Ergodic link failure packet loss
• Non-ergodic link failure G ? G-e
• Compute throughput achievable by
• Network Coding
• Multicast capacity of G-e
• Adaptive Tree Packing
• Re-pack trees on G-e
• Fixed Tree Packing
• Keep trees packed on G
• Delete subtrees downstream from e
• Wu, Chou, and Jain, ISIT 2004

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Robustness to Link Failures
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Robustness to Packet Loss

• Ergodic link failure
• Routing requires link-level error control to
  achieve throughput 95 of sending rate
• Network Coding obviates need for link-level error
  control
• Outputs random linear combinations at
  intermediate nodes
• Delays reconstruction until terminal nodes

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Comparing Network Codingto Multicast Routing

• Throughput v
• Computational complexity v
• Resource cost (efficiency) v
• Robustness to link failure v
• Robustness to packet loss v
• Manageability

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Network Coding for Internet and Wireless
Applications
File download
wireless
Interactive Communication
(conferencing, gaming)
Instant Messaging
Live broadcast
Media on demand
Download from peer
Coded download
Parallel download
Internet
AkamaiRBN
Infra-structure (CDN)
ALM
SplitStreamCoopNet
Digital Fountain
Gnutella Kazaa
BitTorrent
PeerNet
Windows Messenger
XboxLive
Ad Hoc (P2P)
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Live Broadcast

• State-of-the-art Application Layer Multicast
  (ALM) trees with disjoint edges (e.g., CoopNet)
• FEC/MDC striped across trees
• Up/download bandwidths equalized

a failed node
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Live Broadcast (2)

• Network Coding Jain, Lovász, and Chou, 2004
• Does not propagate losses/failures beyond child
• ALM/CoopNet average throughput (1e)depth
  sending rateNetwork Coding average throughput
  (1e) sending rate

failed node
affected nodes(maxflow ht ? ht 1)
unaffected nodes(maxflow unchanged)
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File Download

• State-of-the-Art Parallel download (e.g.,
  BitTorrent)
• Selects parents at random
• Reconciles working sets
• Flash crowds stressful
• Network Coding
• Does not need to reconcile working sets
• Handles flash crowds similarly to live broadcast
• Throughput download time
• Seamlessly transitions from broadcast to download
  mode

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File Download (2)
Mean Max
LR Free 124.2 161
LR TFT 126.1 185
FEC Free 123.6 159
FEC TFT 127.1 182
NC Free 117.0 136
NC TFT 117.2 139
C. Gkantsidis and P. Rodriguez Rodruiguez,
Network Coding for large scale content
distribution, submitted to INFOCOM 2005,
reprinted with permission.
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Instant Messaging

• State-of-the-Art Flooding (e.g., PeerNet)
• Peer Name Resolution Protocol (distributed hash
  table)
• Maintains group as graph with 3-7 neighbors per
  node
• Messaging service push down at source, pops up
  at receivers
• How? Flooding
• Adaptive, reliable
• 3-7x over-use
• Network Coding
• Improves network usage 3-7x (since all packets
  informative)
• Scales naturally from short message to long flows

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Interactive Communication in mobile ad hoc
wireless networks

• State-of-the-Art Route discovery and maintenance
• Timeliness, reliability
• Network Coding
• Is as distributed, robust, and adaptive as
  flooding
• Each node becomes collector and beacon of
  information
• Can also minimize energy

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Network Coding Minimizes Energy (per bit)
a
a,b
a
a
a
b
a,b
b,a
optimal multicasttransmissions per packet 5
network codingtransmissions per packet 4.5

• Wu et al. (2003) Wu, Chou, Kung (2004)
• Lun, Médard, Ho, Koetter (2004)

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Network Coding in Residential Mesh Network
(simulation)
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Physical Piggybacking
s
t

• Information sent from t to s can be piggybacked
  on information sent from s to t
• Network coding helps even with point-to-point
  interactive communication
• throughput
• energy per bit
• delay

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Network Coding in Residential Mesh Network
(multi-session)
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Network Coding with Cross-Layer Optimization
Network Coding has gt 4x bits per Joule
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Summary

• Network Coding is Practical
• Packetization
• Buffering
• Network Coding outperforms Routing on
• Throughput
• Computational complexity
• Resource cost (efficiency)
• Robustness to link failure
• Robustness to packet loss
• Network Coding can improve performance
• in IP or wireless networks
• in infrastructure-based or P2P networks
• for live broadcast, file download, messaging,
  interactive communication applications