Placement%20of%20Continuous%20Media%20in%20Wireless%20Peer-to-Peer%20Networks

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Placement ofContinuous Media in
WirelessPeer-to-Peer Networks

• Shahram Ghadeharizadeh, Bhaskar Krishnamachari,
  Shanshan Song, IEEE Transactions on Multimedia,
  vol. 6, no. 2, April 2004 Presentation by Tony
  Sung, MC Lab, IE CUHK10th November 2003

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Outline

• The H2O Framework
• A Novel Data Placement and Replication Strategy
• Modeling (Topology) and Performance Analysis
• Two Distributed Implementations
• Conclusion, Discussion and Future Work

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The H2O Framework

• H2O Home-to-home online
• A number of devices connected through wireless
  channel.
• Complements existing wired infrastructure such as
  Internet and provide data services to individual
  households.
• Implementing VoD over H2O
• A household may store its personal video library
  on an H2O cloud.
• Each device might act in3 possible roles
• producer of data
• an active client thatis displaying data
• a router that deliversdata from a producerto a
  consumer.

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The H2O Framework

• Media Retrieval
• The 1st block of a clip must make multiple hops
  to arrive.
• A portion of the video must be prefetched before
  playback starts to compensate for network
  bandwidth fluctuations.
• How to Minimize theStartup Latency?

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The H2O Framework

• By Caching
• One Extreme Full replication
• Startup latency is minimized.
• Even if bandwidth is not a limiting factor,
  storage requirement is tremendous.
• A Novel Approach Stripe the video clip into
  blocks, and replicate blocks that has a later
  playback time less often in the system.
• Startup latency is still minimized.

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A Novel Data Placement Replication Strategy

• Parameters
• Video clip X is divided into z equal sized blocks
  bi of size Sb
• Playback duration of a block D Sb / BDisplay
• Playback time of bi (i - 1)D
• Let time to transmit a block across one hop h
• bi can be placed at most Hi hops away under the
  constraint
• Hi ((i - 1)D) / h
• Assumptions
• CBR media, h is a fixed constant, BLink gt
  BDisplay
• b1 should be placed on all nodes in the network.
• For bi with 1 i z, it can be placed less
  often to save storage.

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A Novel Data Placement Replication Strategy

• Core Replication and Placement Strategy
• Divide the clip into z equal sized blocks bi,
  each Sb in size.
• Place b1 on all nodes in the network.
• For each bi with 1 i z, compute its delay
  tolerance Hi .
• Based on Hi , compute ri which is the total
  number of replicas required for block bi . Notes
  that ri and its computation are topology
  dependent, and it decreases monotonically with i
  until it reaches 1.
• Construct ri replicas of block bi and place each
  copy of the block in the network while ensuring
  that for all nodes there exists at least one copy
  of the block bi that is no more than Hi hops away.

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Modeling and Performance Analysis

• Analysis of ri for 3 different topologies
• Worst-Case Linear Topology
• Grid Topology
• Average Case Graph Topology
• Performance is measured in percentage savings
  over full replication.

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Modeling and Performance Analysis

• Worst-Case Linear Topology
• N devices organized in a linear fashion.
• In the worst case scenario, bi must be replicated
  ri N-Hi times and takes N-ri-1 hops to reach
  the destination.
• If ri is non-positive, it is reset to 1.This is
  equivalent to stop replicating those blocks whose
  index exceeds Ur ((N 1)h)/D 1.
• Giving total storage as

contains replica of bi
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Modeling and Performance Analysis

• Worst-Case Linear Topology
• N 1000
• h 0.5 s
• BDisplay 4 Mbps
• Sb 1 MB
• D 2 s

Worst Case Storage Requirement
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Modeling and Performance Analysis

• Grid Topology
• N devices organized in a square grid of fixed
  area, each neighbors only the 4 nodes in each
  cardinal direction.
• No. of replicas ri
• Expected total storage

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Modeling and Performance Analysis

• Grid Topology

Depends on h onlyIndep. of N and SC
2-min Clip
2-h Clip
Decrease StorageIncrease Complexity
Decrease StorageIncrease Complexity
Effect of Block Size
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Modeling and Performance Analysis

• Average Case Graph Topology
• N devices scattered randomly in a fixed area A
  with radio range R for each node.
• Of any given node, the expected number of nodes
  within Hi hops is between
• where ? is a density dependent correction factor
  between 0 and 1 (when densed and nodes are
  distributed evenly).
• Using the upper boundary, the number of replicas
  and expected total storage are

and
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Modeling and Performance Analysis

• Comparison

250 devices, 97 savings
more than 80
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Distributed Implementations

• TIMER and ZONE
• Both control placement of ri copies of each bi
  with the follow objective
• Each node in the network in within Hi hops of at
  least one copy of bi
• General Framework
• The publishing node H2Op computes block size Sb,
  no. of blocks z, and required hop-bound Hi, using
  the previous expressions.
• H2Op floods the network with a message containing
  this information, and each recipient H2Oj
  computes a binary array Aj that signifies which
  blocks to host. The recipients will also retrieve
  from H2Op a copy of the blocks.
• TIMER and ZONE differs in how Aj are computed.

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Distributed Implementations

• TIMER
• A distributed timer-suppress algorithm
• When H2Oj receives the flooded query message, it
  performs z rounds of elections, one for each
  block
• H2Oj determines whether to maintain a copy of
  block bi
• Each node picks a random timer value from 1 to M
  and starts count down.
• When timer reaches 0, and the node is not already
  suppressed
• elects itself to store a copy of block b
• sends a suppress message to all nodes within Hi
  hops
• At the end of a round, every node will either be
  elected or suppressed, and every node is
  guaranteed to be within Hi hops of an elected node

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Distributed Implementations

• ZONE
• Assumes existence of nodes with geopositioning
  info
• Consider all nodes fit within an area of S x S
• For each bi , divide the area into si x si
  squares such that the square fits within a circle
  of radius HiR where R is the radio range
• It can be shown that si ?HiRv2 where ?? 1 is a
  correction factor that depends on node density.
• A copy of bi is placed near the center of each
  square

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Distributed Implementations

• ZONE
• z rounds of elections, one for each block
• All nodes determine which zone they belongs
  to,based on Hi
• For each zone
• Elect the node that is closest to the zone center
  by a distributed leader election protocol (such
  as FloodMax)

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Distributed Implementations

• Comparison

Both distribution algorithms require a few more
replicas per block than predicted
analytically. lt Border Effect The percentage
savings, however, is still several orders of
magnitude superior to full replication.
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Distributed Implementations

• Comparison

Blocks distribution across H2O devices is uniform
with TIMER. ZONE favors placement of blocks with
a large Hi towards the center of a zone. But
results are not provided.
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Conclusion

• Explored a novel architecture collaborating H2O
  devices to provide VoD with minimal startup
  latency.
• A replication technique that replicates the first
  few blocks of a clip more frequently because they
  are needed more urgently.
• Quantified impact of different H2O topologies on
  the storage space requirement.
• Proposed two distributed implementation of the
  placement and replication technique.

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Discussion and Future Work

• Assumptions fixed h
• In wireless ad hoc networks, h is a function of
  the amount of transmitting devices
• Admission control and transmission scheduling
  shall be added to address the variability of h
• Can be extended to adjust data placement when a
  user requests a clip
• Enable H2O cloud to respond to user actions such
  as removal of device
• Consider bandwidth constraints