Download Service Differentiated Peer Selection An Incentive Mechanism for

Service Differentiated Peer
Selection An Incentive Mechanism
for Peer-to-Peer Media Streaming
Ahsan Habib, Member, IEEE, and
John Chuang, Member, IEEE
Outline
Introduction
Related work
Motivation
Proposed incentive mechanism
Evaluation
Conclusion
Introduction(1/2)
P2P system :
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Each peer acts as either client or server
Rely on voluntary resource contributions
Faced problem :
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QoS challenges
Peer selection
Free rider
Introduction(2/2)
Solution :
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Design good peer selection strategies to
realize high quality sessions
Propose an incentive mechanism to encourage
users contribute to system
P2P Media streaming system is different
from traditional P2P file sharing system
Outline
Introduction
Related work
Motivation
Proposed incentive mechanism
Evaluation
Conclusion
Related work
Golle propose a micropayment mechanism
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Earn rewards if upload to other user
Reward can be used in future
Objective:
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Archive maximum cooperation
Ex: KARMA
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Uses a single currency as a way to secure
trading
Related work
Reputation based system:
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Users earn reputation by sharing
Reputation determines peer quality
Downloading from high reputation peer has a
higher probability to obtain better service
Scored based system:
Ex: KaZaA :
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provide download priority to user
User with high score can download files from
user with low scores
Related work
VS. BitTorrent :
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BT doesn' t need any score , reputation ,token
simplicity
BT-like model is less suitable for media
streaming
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BT download random part of a file
BT uses random peer selection model
Incurs random streaming quality
Integrating any supplier selection algorithm
other than random is difficult in this model
Related work
VS taxation model
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Chu propose a taxation model for multicast streaming
Peer with more resources contributes more to system
Peer with limited resources are subsidized by the
system
Ex: SplitStream , CoopNet
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User join several multicast trees
Forwarding load is distributed among all participating
users
Outline
Introduction
Related work
Motivation
Proposed incentive mechanism
Evaluation
Conclusion
P2P Streaming System Case Study
Streaming system :
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Object lookup
Peer-based aggregated streaming
Dynamic adaptations to network and peer
conditions
Quality of a peer depend on:
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Availability
Offered rate
capacity of outgoing link
P2P Streaming System Case Study
Cope with fluctuation in network service:
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Forward error correction (FEC)
Multidescription coding
Sending rates adjustment
Dynamic switching mechanism
This paper uses PROMISE as an example
system
Session : A peer requests a media file
issues a lookup request to the underlying
P2P substrate
Impact of Noncooperation(1/2)
Define quality of a streaming session:
T: total number of packets in a streaming session
Zi :
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Zi =1 if packet i arrives before its scheduled play-out time
Zi =0 otherwise
The system quality is defined as the average
quality of all receivers in the system.
Impact of Noncooperation(2/2)
Map Q into MOS (mean option score)
5% loss rate result in MOS score 4
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90% of the frames are good
25% loss rate
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MOS ≤2
50% frames experience poor quality
Q ≤0.75 ,almost half of the frames
experience bad quality!
Cooperation brings quality
Simultaneous uploading hurts
quality
Random peer selection
provides random quality
Suppliers are chosen arbitrarily
Random peer selection
provides random quality
Known and good peers are chosen as suppliers
Outline
Introduction
Related work
Motivation
Proposed incentive mechanism
Evaluation
Conclusion
Proposed incentive mechanism
P2P systems in general are characterized by
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Large populations
Asymmetries of interest
score-based incentive mechanism
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The contribution level of a user is converted
into a score, which in turn is mapped into a
percentile rank that determines the rank of the
user among other users in the system.
Converting the contribution of a
user into a score
Related functions
Contribution function X, depends on its action a and
a random output distortion Θ
Utility U is a function of the streaming session quality
Q and the contribution cost C.
The behavior of the overall system is defined by a
social welfare function
Related functions
:The highest possible quality provided by the system
: Initial value
User’s expected utility
Rank-order theory
Utility of a peer varies from
to
Newcomers and free-riders are treated identically
Prevent whitewashing attack if identity costs are cheap
Free-rider only exploits others in duration!
Receiver only can choose peer with equal or lower rank to
be its supplier
Scoring Function
Either consider contribution or both the
contribution and consumption by user
Could also take into account the difference
in demand for different resources in the network.
Percentile Rank Computation
Why Rank?
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Knowledge of one’s score is not sufficient for predicting the expected
quality to be received by the user
Using cdf to compute percentile score
Individual nodes can locally estimate their rank based on a
sample of user scores
Percentile rank only used for prediction purposes
rank computation at any time is done among the users
that are interested in a particular media.
Percentile Rank Computation
Quality Function
Mapping rank into quality, so that user can
predict its quality based on their rank
Definition:
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Ns: Total number of good supplier
Gi: Good supplier for receiver i
Quality Function
Outline
Introduction
Related work
Motivation
Proposed incentive mechanism
Evaluation
Conclusion
Setup
We simulate the incentive mechanism using
ns-2 and PROMISE simulation module
Hierarchical topology
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Highest level represent ISP
 Link delay: 100ms
 Capacity varied from 1.5Mbps to 5Mbps
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second level stub domains
 Link delay: 10ms
 Link capacity: the same distribution as the transit domain
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Lowest level(600 routers and 1200 peers)
 Link delay: 10ms
 Link capacity: 1.2Mbps
Setup
Peer availability varied from 0.1~0.9
In each experiment ,Run the model for
1000–2000 rounds
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Each peer supplies in 20 sessions and
receives 5 times in this experiment
Using PROMISE implementation as an
underlying streaming system in the
Planet-Lab test-bed to conduct wide
area experiments.
Peer Selection
Peer Selection
Quality of Service
Quality of Service
Outline
Introduction
Related work
Motivation
Proposed incentive mechanism
Evaluation
Conclusions
Conclusions(1/2)
We showed that a rank order-based incentive
mechanism achieves cooperation through service
differentiation
Contribution Score, Score  Rank
Rank determines the quality of a streaming
session.
The rank is estimated in a scalable way without
involving all users in the system.
Conclusions(2/2)
Experimental evaluation shows that the
incentive mechanism provides near optimal
quality
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Reduces overhead in sending redundant data
Future work:
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Add empirical data to refine incentive mechanism