Peer-To-Peer%20Multimedia%20Streaming%20Using%20BitTorrent

1
Peer-To-Peer Multimedia Streaming Using BitTorrent

• Purvi Shah, Jehan-François Pâris
• University of HoustonHouston, TX

2
Problem Definition
One Server
Transferring videos is a resource intensive task!
ManyCustomers

• Objectives
• Customer satisfactionMinimize customer waiting
  time
• Cost effectivenessReduce operational costs
  (mostly hardware costs)

3
Transferring Videos

• Video download
• Just like any other file
• Simplest case file downloaded using conventional
  protocol
• Playback does not overlap with the transfer
• Video streaming from a server
• Playback of video starts while video is
  downloaded
• No need to wait until download is completed
• New challenge ensuring on-time delivery of data
• Otherwise the client cannot keep playing the video

4
Why use P2P Architecture?

• Infrastructure-based approach(e.g. Akamai)
• Most commonly used
• Client-server architecture
• Expensive Huge server farms
• Best effort delivery
• Client upload capacity completely unutilized
• Not suitable for flash crowds

5
Why use P2P Architecture?

• IP Multicast
• Highly efficient bandwidth usage
• Several drawbacks so far
• Infrastructure level changes make most
  administrators reluctant to provide it
• Security flaws
• No effective widely accepted transport protocol
  on IP multicast layer

6
P2P Architecture

• Leverage power of P2P networks
• Multiple solutions are possible
• Tree based structured overlay networks
• Leaf clients bandwidth unutilized
• Less reliable
• Complex overlay construction
• Content bottlenecks
• Fairness issues

7
Our Solution

• Mesh based unstructured overlay
• Based on widely-used BitTorrent content
  distribution protocol
• A P2P protocol started 2002
• Linux distributors such as Lindows offer software
  updates via BT
• Blizzard uses BT to distribute game patches
• Start to distribute films through BT this year

8
(No Transcript)
9
BitTorrent (I)
10
BitTorrent (II)

• Has a central tracker
• Keeps information on peers
• Responds to requests for that information
• Service subscription
• Built-in incentives Rechoking
• Give preference to cooperative peers Tit-for-tat
  exchange of content chunks
• Random search Optimistic un-choke
• When all chunks are downloaded, peers can
  reconstruct the whole file
• Not tailored to streaming applications

11
Evaluation Methodology

• Simulation-based
• Answers depend on many parameters
• Hard to control in measurements or to model
• Java based discrete-event simulator
• Models queuing delay and transmission delay
• Remains faithful to BT specifications

12
BT Limitations

• BT does not account for thereal-time needs of
  streaming applications
• Chunk selection
• Peers do not download chunks in sequence
• Neighbor selection
• Incentive mechanism makes too many peers to wait
  for too long before joining the swarm

13
Chunk Selection Policy

• Replace BT rarest first policy by a sliding
  window policy
• Forward moving window is equal to viewing delay

chunk not yet received
Download window
missed chunk
received chunk
playback start
playback delay
14
Two Options

• Sequential policy
• Peers download first the chunks at the beginning
  of the window
• Limit the opportunity to exchange chunks between
  the peers
• Rarest-first policy
• Peers download first the chunks within the window
  that are least replicated among its neighbors
• Feasibility of swarming by diversifying available
  chunks among peers

15
Best
Worst
16
(No Transcript)
17
Discussion

• Switching to a sliding window policy greatly
  increases quality of service
• Must use a rarest first inside window policy
• Change does not suffice to achieve a satisfactory
  quality of service

18
Neighbor Selection Policy

• BT tit-for-tat policy
• Peers select other peers according to their
  observed behaviors
• Significant number of peers suffer from slow
  start
• Randomized tit-for-tat policy
• At the beginning of every playback each peer
  selects neighbors at random
• Rapid diffusion of new chunks among peers
• Gives more free tries to a larger number of peers
  in the swarm to download chunks

19
(No Transcript)
20
(No Transcript)
21
Discussion

• Should combine our neighbor selection policy
  with our sliding window chunk selection policy
• Can then achieve an excellent QoS with playback
  delays as short as 30 s as long as video
  consumption rate does not exceed 60 of network
  link bandwidth.

22
Comparison withClient-Server Solutions
23
Chunk size selection

• Small chunks
• Result in faster chunk downloads
• Occasion more processing overhead
• Larger chunks
• Cause slow starts for every sliding window
• Our simulations indicate that 256KB is a good
  compromise

24
(No Transcript)
25
Premature Departures

• Peer departures before the end of the session
• Can be voluntary or resulting from network
  failures
• When a peer leaves the swarm, it tears down
  connections to its neighbors
• Each of its neighbors to lose one of their active
  connections

26

• Can tolerate the loss ofat least 60 of the
  peers

27
Future Work

• Current work
• On-demand streaming
• Robustness
• Detect malicious and selfish peers
• Incorporate a trust management system into the
  protocol
• Performance evaluation
• Conduct a comparison study

28
Thank You Questions?

• Contact purvi_at_cs.uh.edu
• paris_at_cs.uh.edu

29
Extra slides
30
nVoD

• Dynamics of client participations, i.e. churn
• Clients do no synchronize their viewing times
• Serve many peers even if they arrive according to
  different patterns

31
Admission control policy (I)

• Determine if a new peer request can be accepted
  without violating the QoS requirements of the
  existing customers
• Based on server oriented staggered broadcast
  scheme
• Combine P2P streaming and staggered broadcasting
  ensures high QoS
• Beneficial for popular videos

32
Admission control policy (II)

• Use tracker to batch clients arriving close in
  time form a session
• Closeness is determined by threshold ?
• Service latency, though server oriented, is
  independent of number of clients
• Can handle flash crowds
• Dedicate ? channels for each video making worst
  service latency, w ? D/?

33
Results

• We use the M/D/? queuing model to estimate the
  effect on the playback delay experienced by the
  peers