CoopNet: Cooperative Networking

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CoopNet Cooperative Networking

• Phil Chou, Venkat Padmanabhan, Helen Wang
• September 17, 2002

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Motivation

• Flash crowd can overwhelm server
• often due to news event of widespread interest
• but not always (e.g., Webcast of birthday
  party)
• can affect relatively obscure sites (e.g.,
  election.dos.state.fl.us, firestone.com,
  nbaa.org)
• affects Web content as well as streaming content
  (live and on-demand)
• infrastructure-based CDNs arent for everyone
• Goal solve the flash crowd problem!

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Cooperative Networking
CoopNet
Client-server
Pure peer-to-peer

• CoopNet complements client-server system
• Client-server operation in normal times
• P2P content distribution invoked on demand to
  alleviate server overload
• Clients participate only while interested in the
  content
• Server still plays a critical role

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Research Activities

• Web flash crowd alleviation (with Kay
  Sripanidkulchai)
• evaluation using Sep 11 traces from MSNBC
• prototype implementation done
• paper _at_ IPTPS 02
• MDC-based streaming media distribution
• evaluation using Sep 11 traces from MSNBC,
  Akamai, Digital Island
• implementation in progress
• paper _at_ NOSSDAV 02
• patent process in progress
• initial discussions with Digital Media Division

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Research Activities (contd.)

• PeerMetric (with Karthik Lakshminarayanan)
• characterize broadband network performance
• P2P as well as client-server performance
• working with Xbox Online (Mark VanAntwerp)
• deployment on 25 distributed nodes underway
• eventual deployment on 300 Xbox Live beta users
• Future directions
• CoopNet in a Wireless Mesh Network
• good synergy saves Internet bandwidth, improves
  robustness

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Robustness of Live Streaming

• Peers are not dedicated servers ? potential
  disruption due to
• node departures and failures
• higher priority traffic
• Traditional ALM is not sufficient

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Traditional Application-level Multicast
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CoopNet Approach to Robustness

• Multiple description coding (MDC)
• Multiple, diverse distribution trees

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Multiple Description Coding
Layered coding
MDC

• Unlike layered coding, there isnt an ordering of
  the descriptions
• Every subset of descriptions must be decodable
• Modest penalty relative to layered coding

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Multiple Description Coding

• Simple MDC
• every Mth frame forms a description
• More sophisticated MDC combines
• layered coding
• Reed-Solomon coding
• priority encoded transmission
• optimized bit allocation

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Multiple Distribution Trees
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Tree Management

• Goals
• short and wide trees
• efficiency
• diversity
• quick join and leave processing
• scalability
• CoopNet approach centralized protocol anchored
  at the server

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Centralized Tree Management

• Basic protocol
• nodes inform server of their arrival/departure
• server tracks node capacity and tells new nodes
  where to join
• each node monitors its packet loss rate and takes
  action when the loss rate becomes too high
• simple, should scale to 1000 joins/leaves per
  sec.
• Optimizations
• delay coordinates to estimate node proximity
  (à la GeoPing)
• achieving efficiency and diversity
• migrate stable nodes to a higher level in the
  tree

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Achieving Efficiency and Diversity
NY
SEA
S
Supernode
SF
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MDC versus SDC
Based on MSNBC traces from Sep 11
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CoopNet Transport Architecture
Embedded Stream
GOF
Parse
Packetize
ZSF
Optimize (M, p(m))
Break Points
RD Curve
M descriptions
RS Encoder
Server
Internet
Depacketize
Embedded Stream(truncated)
Reformat
Decode
Render
GOF(quality depends on descriptions received)
m M descriptions
RS Decoder
Client
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Summary

• Robustness is the key challenge
• MDC with multiple distribution trees improves
  robustness in peer-to-peer media streaming
• Centralized tree management is efficient and can
  scales

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Current Activity

• CoopNet media streaming system is being built
• Evaluation
• adaptability
• server scalability
• media stream quality
• overhead in MDC and control protocol
• Dealing with client heterogeneity
• combine MDC with layering

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Issues

• Firewall and NAT traversal
• Digital Right Management issues
• ISP pricing policies
• Enterprise scenarios

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Quality During Multiple Failures
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Quality During Single Failure