Bullet: High Bandwidth Data Dissemination Using an Overlay Mesh

1
Bullet High Bandwidth Data Dissemination Using
an Overlay Mesh

• by
• Dejan Kostic, Adolfo Rodriguez,Jeannie Albrecht
  and Amin Vahdatpresented byJon Turner

2
Introduction

• Problem large-scale data dissemination.
• focus on high bandwidth streaming media
• Current solutions
• IP multicast not complete solution and not
  deployed
• overlay multicast too dependent on quality of
  multicast trees
• Proposed approach.
• partial distribution using multicast tree
• all data distributed, but each node gets only
  fraction from parent
• peer-to-peer distribution of remainder
• periodic distribution of information about data
  present at random subsets of entire group
• nodes request missing data from peers
• system combines number of elements developed
  earlier
• erasure encoding (redundant tornado codes)
• random subset distribution
• informed content-delivery
• TCP friendly rate control

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Overview of Bullet Operation

• Distribute data over tree
• limited replication
• uses bandwidth feedback
• Nodes retrieve missing data from peers.
• Periodic distribution of content availability
  info.
• random subset of nodes
• summary of their content
• Limited set of peering relationships.
• each node receives from limited set of senders
• each node limits receivers
• sets evolve over time to improve utility of peers
• Data sent using TCP friendly rate control.

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Data Distribution

• System operates in series of epochs (typ. 5
  seconds).
• at start of epoch, nodes learn of descendants
  children have
• child i is assigned a sending factor sfi equal to
  its share of descendants (child with 20 of
  descendants has sfi.2)
• Nodes forward data received from parent to
  children.
• packets are assigned to children according to
  their sf values
• additional copies forwarded to children with
  spare bandwidth
• use limiting factors, lfi to determine which
  children have spare bw
• lf values are adjusted dynamically in (0,1 based
  on bw
• Algorithm sketch executed for each input packet
  p

• Find child t that has been assigned fewer than
  its share of packets.
• attempt to send p to t (transport protocol blocks
  send if sending rate too high)
• if attempt succeeds, assign p to t

• For each child c
• attempt to send to c if no successful attempt yet
  or if c has spare bw
• if attempt succeeds and this is first successful
  attempt, assign p to c
• if attempt succeeds, but not first success,
  increase lfc
• if attempt fails, decrease lfc

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Finding Prospective Data Sources

• At start of each epoch, each node receives
  information about data present at a random subset
  of peers.
• summary ticket describing data present at each
  peer from current working set
• by comparing peer summary tickets to its own,
  node determines similarity of peers data to its
  data
• select new peers with dissimilar data sets
• limit on number of concurrent senders
• discard senders that have been supplying too few
  new packets
• creates space in sender list for new sender
• Computing summary ticket
• if node has packets i1,i2,...,in
• let tjminfj(i1), fj(i2),... for 1jk where
  fj(ik)(aj ikbj) mod U
• summary ticket is (t1, t2,....,tk)
• each tj depends on the entire sequence
• similarity of two tickets is fraction of values
  in common

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Recovering Data From Peers

• Node periodically supplies its senders with a
  representation of the set of packets it currently
  has.
• limited to current working set (range of
  sequence numbers)
• set is represented by a Bloom filter
• each sender is also assigned a fraction of the
  working set
• packets with sequence numbers equal to i mod s
  where s is number of senders
• Senders transmit missing packets using available
  bw.
• packets checked against Bloom filter
• dont send if packets sequence number is in
  Bloom filter
• because senders selected for dissimilarity, most
  packets should pass check
• Example of numerical parameters.
• 5 second epoch, 30 second working set, 500
  Kb/s50 p/s,10 senders
• so asking each sender for at most 150 packets
• simpler and possibly more efficient to use bit
  vector

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Distributing Random Subsets

• Objective give each node a random sample of
  other nodes in the tree, excluding its
  descendants.
• Two phases
• collect phase propagate random subsets up the
  tree
• distribute phase propagate random subsets back
  down tree
• mix subset received from parent with child subsets

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Other Features

• Formation of overlay tree.
• not a focus of this work
• paper cites variety of previous tree construction
  algorithms
• argues that use of mesh makes tree quality less
  important
• most results use random tree
• Data encoding
• not a focus of this work
• suggests use of erasure codes or
  multiple-description codes
• reported results neglect encoding
• Transport protocol
• unreliable version of TFRC
• adjusts sending rate to match fair-share
  bandwidth based on detected loss probability
• feedback from transport sender used by Bullet to
  adjust rates at which it attempts to send to
  children

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Performance Evaluation

• Most results emulated in ModelNet (Duke network
  emulation system, similar to Emulab)
• uses 50 machines (2 GHz P4s running Linux) to
  emulate 1,000 node overlay
• 20,000 node network topology generated using INET
• link delays determined by geographic separation
• random subset of network client nodes selected
  for overlay
• random node in overlay selected as root
• Link bandwidths
• four classes of links
• three scenarios

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Tree Quality for Streaming Data
bottleneck bandwidth tree constructed
heuristically to have no small capacity
links(off-line construction)
point is to show that bottleneck tree is much
better than random tree used by Bullet.
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Baseline Bullet Performance
average better than bottlneck tree for streaming
case
not too much excess traffic
3s point suggests a few percent get less than
half average
most data from mesh
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Cum. Dist. of Received Bandwidth
median about 525 Kb/s
about 100 nodes get about 50 nodes get 13
Comparing Bullet to Streaming
when plenty of bandwidth both do well
Bullet does better when bandwidth limited
much better when bandwidth scarce
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Effect of Oblivious Data Distribution
tree nodes attempt to forward all received
packets to all children
average throughput drops by 25
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Bullet vs Epidemic Routing

• Push gossiping
• nodes forward non-duplicate packets to randomly
  chosen set of peers in their local view
• packets forwarded as they arrive
• no tree required
• Streaming with anti-entropy
• data streamed over multicast tree
• gossip with random peers to retrieve missing data
• anti-entropy (?) used to locate missing data
• apparently, this means periodically select a
  random peer and send it list of missing packets,
  peer supplies missing packets it has
• Experiments done on 5000 node topology.
• no physical losses
• link bandwidths chosen from medium range

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Bullet vs Epidemic Routing
gossiping has high overhead,bullet and streaming
w/AE are comparable
bullet outperforms epidemic routing
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Performance on Lossy Links
non-transit links lose 0 to .3 of
packetstransit links lose 0 to .15 of links
lose 5 to 10
Bullet dramatically better than streaming over
bottleneck tree topology
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Performance with Failing Node

• Child of root with 110 descendants fails at time
  250.
• Assume no underlying tree recovery.
• Evaluate effect of failure detection and
  establishment of new peer relationships

adding new peers eliminates degradation
degraded throughput using existing peers
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Bullet Performance on Planet Lab

• 47 nodes with 10 in Europe, including root.
• Compare to streaming over hand-crafted trees.

European nodes all near tree root
select children of root to have poor bandwidth
from root,recurse
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Related Work

• Peer-to-peer data dissemination
• Snoeren et. al. 2001
• Fast Replica Cherkasova Lee 2003
• Kazaa and Bit Torrent
• Epidemic data propagation
• pbcast Birman et. al. 1999
• lbpcast Eugster et. al. 2001
• Multicast
• Scalable Reliable Multicast Floyd et. al.
  1997
• Narada Chu et. al. 2000
• Overcast Janotti et. al. 2000
• Content streaming
• Split Stream Castro et. al. 2003
• CoopNet Padmanabhan et. al. 2003

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Conclusions

• Overlay mesh is superior to streaming over
  overlay multicast tree.
• Bullet demonstrates this and includes some novel
  features.
• method of distributing data to subtrees to
  equalize availability
• scalable method of finding peers who can supply
  missing data
• Large-scale performance evaluation
• supports claims for Bullets performance
  advantages
• explores performance under variety of conditions

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Discussion Questions

• What are the contributions?
• whats novel? whats borrowed?
• does it represent an improvement? how much?
• are the authors claims justified?
• for what applications might system be useful?
• Are the authors design choices adequately
  justified?
• method for distributing data over tree?
• acquiring information about peer data?
• use of summary tickets? use of Bloom filters? use
  of TFRC?
• Is performance evaluation satisfactory?
• what about other network topologies, link
  bandwidths?
• what about different group sizes? tree
  characteristics?
• why no detailed examination of specific design
  choices?
• why isnt cross traffic emulated?
• what about processing requirements? whats the
  average number of cycles executed per packet
  received?
• is it repeatable by others?