On Reducing Mesh Delay for Peer-to-Peer Live Streaming

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On Reducing Mesh Delay for Peer-to-Peer Live
Streaming

• Dongni Ren, Y.-T. Hillman Li, S.-H. Gary Chan
• Department of Computer Science and Engineering
  The Hong Kong University of Science and
  Technology
• INFOCOM 2008
• Junction

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Design goals

• overlay
• Low delay
• Robust to user churn
• Accommodation of asymmetric bandwidths
• Distributed, simple and adaptive

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Tree Mesh

• Tree
• Achieve low delay
• Cannot accommodate well network dynamics and
  asymmetric bandwidth
• Mesh
• Robust to user churn
• Asymmetric bandwidth is overcomed by aggregating
  the bandwidth of multiple parents to guarantee a
  certain streaming rate.

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Delay in Mesh

• Mesh delay
• Due to the longest path from the node to the
  source out of all its parents.
• The number indicated in the square boxes
• Packet scheduling delay
• Due to packet transmission time and scheduling
  policy of a peer with its parents of
  heterogeneous bandwidth.

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Optimization of mesh delay

• Problem formulation and a centralized heuristic
• To form a mesh which minimizes the maximum delay
  of the peers in the network while meeting a
  certain streaming rate requirement.
• Centralized heuristic as a benchmark
• A distributed protocol for low-delay mesh
• Power concept
• Adaptation mechanism
• Performance study on the algorithms
• Simulation
• Compare with traditional and state-of-the-art
  approaches
• Outreach
• Closest-parents

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Problem Formulation

• Minimum Delay Mesh Problem (MDM problem)
• To find a mesh which minimizes the maximum of the
  peer delay
• MDM problem is NP-Hard
• Traveling Salesman Problem (TSP) can be reduced
  to MDM problem

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A Centralized Heuristic

• Shallow streaming mesh
• Peers are close to the source with low hop count
• Put the nodes with high uplink bandwidth close to
  the source to increase the fanout of the mesh
  towards the uplink.
• Power
• Achieve a balance between the delay and uplink
  bandwidth
• Power is defined as the throughput divided by
  delay

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Algorithm

• Rank all the nodes according to their uplink
  capacities divided by their delay to the source
• Push them into the mesh in descending order
• node i is pushed into the mesh
• calculate the power Pi(j) for all the nodes
  already in the mesh
• connect node i to node j with the largest Pi(j)
  value
• If node i is not fully served by node j, connect
  node i to one more parent with the second largest
  Pi(j) value

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Power-Based Distributed Algorithm

• Rendezvous Point
• Caches a list of recently arrived peers
• Returns a few of them to the newcomer (potential
  parents)
• Same as centralized heuristic
• If the peers returned by the Rendezvous Point
  cannot fully serve the newcomer , the newcomer
  request the neighbor of those peers.

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Adaptation

• With high probability, there are some
  low-bandwidth peers occupying the areas in
  between the source and the powerful ones.
• Request Step
• Grant Step
• Accept Step

TTL gt 0, decrease and forward to its parent Its
uplink BW gt senders, GRANT
GRANT
parent
REQUEST childs uplink BW time-to-live
(TTL)
Among these the ancestor with shortest distance
from the source is picked.
child
Its residual BW gt streaming rate
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Simulation Results

• Simulation setup and metrics
• Brite generate 10 two levels top-down
  hierarchical topology
• 8 autonomous systems each of which has 624
  routers
• Bandwidth distribution

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Evaluation Metrics

• Delay
• The time taken for data to travel from the
  streaming server to the peers
• Average source to end among all peers
• Maximum source to end delay of the mesh
• Hop Count
• The number of intermediate peers involved on the
  overlay path form the source to a peer.
• Source Workload
• The amount of bandwidth consumed at the source

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Simulation Results

• Adaptation
• Average delay reduces
• Variance narrows down

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Simulation Results

• Delay
• Power scheme outperforms the other two as the
  number of peers grows

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Simulation Results

• Hop Count
• The power scheme gives a more compact mesh than
  Outreach
• High BW peers in Power scheme are aggressively
  promoted upwards and thus more branches occur
  near the source.

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Simulation Results

• Source Workload
• Outreach actively places peers under source -gt
  rely on source
• Power and closest parent scheme, the source
  contribution roughly the same

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Simulation Results

• TTL
• Number of Adaptation Change the number existing
  connections that are broken in the adaptation
  phase before the mesh reaches a static state
• The cost of adaptation proportional to the number
  of adaptation happened

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Simulation Results

• Delay Reduction
• Average ratio of average delay reduced by
  adaptation to average delay without adaptation
• Maximum ratio of maximum delay reduced by
  adaptation to maximum delay without adaptation
• Having large TTL value only gives slightly better
  benefit.
• Risk of flooding the overlay

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Contribution Conclusion

• The first body of work addressing the
  optimization of mesh delay for P2P streaming
• Not mention much about how the algorithm tolerate
  the churn ( peer join or leave )