hybrid peer-to-peer media distribution systems: a performance study yicheng tu, jianzhong sun and...
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![Page 1: Hybrid Peer-to-Peer Media Distribution Systems: a Performance Study Yicheng Tu, Jianzhong Sun and Sunil Prabhakar Department of Computer Sciences, Purdue](https://reader035.vdocument.in/reader035/viewer/2022062517/56649e765503460f94b77859/html5/thumbnails/1.jpg)
Hybrid Peer-to-Peer Media Distribution Systems: a
Performance Study
Yicheng Tu, Jianzhong Sun and Sunil Prabhakar
Department of Computer Sciences, Purdue University
Paper published in ACM/SPIE Conference on Multimedia Computing and Networking (MMCN04)
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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)
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Peer-to-Peer in Media Streaming
• CDNs are expensive to build• Investment increases as popularity of content
does • 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.
Gnutella)– Limited/heterogeneous contributions from peers– Many-to-one streaming, difficult to synchronize – Duration of peer contribution (Peer failure)
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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)
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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
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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 > servers
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(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 bottleneck
• Peer has infinite storage *• Peer never fails *• Requests are uniformly distributed among
the peer population
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Metrics
• System capacity:– total bandwidth of servers + qualified peers
• Server-peer transition time (k0) ***• Reject rate
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Intuitively • 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
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Mathematically
Note α/b is the Capacity Growth Factor, the above can be transformed into
)()()1( kPb
NkPkP
11)(
k
b
NkP
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More on Mono-file System
In a system with requesting rate λ, the condition for server-peer transition is:
We get k0 as:
Lbb
Nk
0
1
b
NLb
N
Lbk
b
1lg
lg)lg(log
)1(0
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What About Multi-file Systems?
• Previous framework cannot be applied here directly
• 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
NNF
ff
1
F
ff
1
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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 – λf is also unknown, but it doesn’t matter
• Lets forget about the real solution to k0 for a while and think about the optimal solution !!
b
NLbk ff
f
1lg
lg)lg(,0
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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} (0≤f≤F )Subject to ,
1
NNF
ff
,1
F
ff
.1lg
lg)lg(,0
b
NLbk ff
f
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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 λf
– Surprisingly, the optimal k0 can be expressed by the same formula for mono-file system
N
Lb
N
Lb
N
Lb
N
Lb
N
Lb
i
i
F
F
2
2
1
1
NN ff
b
NLbk
1lg
lg)lg(0
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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 bNλf/λ > 10, very close !
• What about the assumption of independence among subsystems– We introduce an "independence coefficient”β– βis close to 1 when the pool size M is big– Good thing: M should be and is big in general
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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 γ • For any streaming period k, γ= Pr { X ≥T(k)+ L | X >T(k)} where T(k) is the starting time for period k.
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Effects of Peer Failures
• Generally,γis difficult to get– It changes with age (k)– More specifically, it depends on the age structure
• 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
LsekTXLkTX )}(|)(Pr{
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Effects of Peer Failures
• With a universalγvalue,
• Everything else is the same• The transition time
• Note the Capacity Growth Factor becomesγ(1+α/b)
)()()1( kPb
NkPkP
b
bNLbb
k
1lg
)1()1(lg
0
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Experimental Results
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Experiments: Effects of α
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Effects of λ
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Effects of Media Number
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Number of Peers by Storage Use
Experiments
k0 (h) 1 2 3 4 β
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
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Effects of Peer Failure
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Conclusions
• 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
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References
• 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 2002.