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Hybrid Peer-to-Peer Media Distribution Systems: a Performance Study


Contention Distribution Networks (CDNs) Peer-to-Peer in Media Streaming. CDNs are expensive to build. Investment increases as popularity of content does ... – PowerPoint PPT presentation

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Title: Hybrid Peer-to-Peer Media Distribution Systems: a Performance Study

Hybrid Peer-to-Peer Media Distribution Systems a
Performance Study
  • Yicheng Tu, Jianzhong Sun and Sunil Prabhakar
  • Department of Computer Sciences, Purdue
  • Paper published in ACM/SPIE Conference on
    Multimedia Computing and Networking (MMCN04)

Media Distribution
  • Streaming needed
  • QoS important
  • Network bandwidth is the bottleneck
  • Multicast CNN.COM
  • Unicast online cinema
  • We concentrate on the latter
  • Server-based system lacks sufficient capacity
  • Improve capacity by proxies
  • Contention Distribution Networks (CDNs)

Peer-to-Peer in Media Streaming
  • CDNs are expensive to build
  • Investment increases as popularity of content
  • Peer-to-Peer(P2P) approach
  • The idea Utilize bandwidth among clients (peers)
  • Inexpensive
  • Capacity grows as popularity does
  • Problems of P2P systems
  • Object searching is slow in pure P2P system (e.g.
  • Limited/heterogeneous contributions from peers
  • Many-to-one streaming, difficult to synchronize
  • Duration of peer contribution (Peer failure)

Hybrid System CDN P2P
  • Combine the advantages of both CDN and P2P
  • Increase of bandwidth by a P2P community
  • Search is done by a centralized directory server
  • Assume object updating is of reasonable frequency
  • A small number of seed servers
  • Used for streaming
  • Boot up the system
  • Complementary bandwidth source in case of failure
  • System model and failure-resistant streaming
    protocol proposed by Xu et al. (2002) and Heefeda
    et al.(2003)

This Research
  • Our Goal To study the system dynamics of the
    aforementioned hybrid media streaming system
  • Our approach mathematical analysis
  • Non-trivial, a good model is the key
  • Previous attempt (Xu et al., 2002) gives no
    analytical results
  • Confirm analysis by large-scale simulation

System Model
  • Players
  • Directory Server
  • Servers
  • Same name Streaming servers, CDN servers
  • Peers (clients)
  • Requesting peer
  • Supplying peer
  • Qualified peer
  • Media objects
  • Operations
  • Order of streaming entities
  • peers gt servers

(Initial) Assumptions
  • Only one object in the system and they are of the
    same streaming length (L) and bitrate(b)
  • The server side upload link is always the
  • Peer has infinite storage
  • Peer never fails
  • Requests are uniformly distributed among the peer

  • System capacity
  • total bandwidth of servers qualified peers
  • Server-peer transition time (k0)
  • Reject rate

  • System capacity growth analogous to population
    growth of a single species in a biological system
  • Servers and supplying peers give birth to
    requesting peers
  • Each streaming cycle equals a generation
  • Exponential growth

  • Note a/b is the Capacity Growth Factor, the above
    can be transformed into

More on Mono-file System
  • In a system with requesting rate ?, the condition
    for server-peer transition is
  • We get k0 as

What About Multi-file Systems?
  • Previous framework cannot be applied here
  • Difficult to model the interactions between
    per-file proliferation
  • Analysis in a rather indirect" way
  • View system as a combination of F independent
    subsystems with and
  • Statistical multiplexing (reality) vs. Sharing
    Multiplexing (our view)
  • Then prove the above view is close to reality

k0 in Multi-file System
  • Each subsystem follows previous analysis
  • Still it is hard to get k0 for the whole system
  • System-level k0 depends on distribution of Nf
  • Nf is unknown
  • ?f is also unknown, but it doesnt matter
  • Lets forget about the real solution to k0 for a
    while and think about the optimal solution !!

Optimizing System-level k0
  • An observation k0 is the maximum of all k0,f
  • System reaches transition only when all
    single-file subsystem do
  • The optimization
  • Minimize k0 max k0,f (0fF )
  • Subject to

Optimal Choice of Nf
  • The above optimization has solution
  • k0 k0,1 k0,2 k0,F
  • Putting into the k0,f formula
  • And for all f, we get
  • What does this mean?
  • The optimal choice of Nf is directly related to
  • Surprisingly, the optimal k0 can be expressed by
    the same formula for mono-file system

To Make the Story Complete
  • We proved the system converges to the optimal
    distribution of server bandwidth (Nf)
  • We used confidence intervals to analyze how close
    the system is to the optimal situation
  • When bN?f/? gt 10, very close !
  • What about the assumption of independence among
  • We introduce an "independence coefficientß
  • ßis close to 1 when the pool size M is big
  • Good thing M should be and is big in general

Effects of Peer Failures
  • Critical feature of any P2P system, cannot ignore
  • Relate to the biological model individuals die
  • Model failures by assigning a lifespan to each
    peer, denoted as a random number X
  • Assume a survival rate ?
  • For any streaming period k,
  • ? Pr X T(k) L X gtT(k)
  • where T(k) is the starting time for period k.

Effects of Peer Failures
  • Generally,?is difficult to get
  • It changes with age (k)
  • More specifically, it depends on the age
  • Previous study (Saroiu et al., 2002)shows that
    peer lifespan follows an exponential distribution
  • Revisit the survival rate,
  • where s is the average lifespan.
  • The next steps become easy

Effects of Peer Failures
  • With a universal?value,
  • Everything else is the same
  • The transition time
  • Note the Capacity Growth Factor becomes?(1a/b)

Experimental Results
Experiments Effects of a
Effects of ?
Effects of Media Number
Number of Peers by Storage Use
Experiments k0 (h) 1 2 3 4 ß
F 1 8 10319 0 0 0 1.000
F 50 8 11844 33 0 0 0.997
F 100 8 10245 23 0 0 0.998
F 250 9 12209 54 0 0 0.991
F 500 11 16694 158 1 0 0.981
F 1000 13 19260 324 1 0 0.967
Effects of Peer Failure
  • The hybrid streaming system follows an
    exponential growth pattern
  • The Capacity Growth Factor affects system
    performance more than other factors do
  • Within some boundary, capacity growth of
    multi-file and mono-file systems can be described
    by the same equation
  • Peer failures have significant effects on system
    capacity, it could kill the system
  • Quantitative analysis of complex system is hard,
    but doable in some cases

  • D. Xu, H-K. Chai, C. Rosenburg and S. Kulkarni.
    Analysis of a Hybrid Architecture for
    Cost-Effective Streaming Media Distribution. In
    Proc. of ACM/SPIE MMCN 2003,January 2003.
  • M. Hefeeda,  A. Habib, B. Botev, D. Xu,  B.
    Bhargava, PROMISE  Peer-to-Peer Media Streaming 
    Using CollectCast. In Proc. of  ACM Multimedia
    2003, Berkeley, CA,  November 2003
  • S. Saroiu, P. K. Gummadi and S. D. Gribble. A
    Measurement Study of Peer-to-Peer File Sharing
    Systems. In Proc. of ACM/SPIE MMCN 2002,January
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