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 : Each peer acts as either client or server Rely on voluntary resource contributions Faced problem : QoS challenges Peer selection Free rider

Introduction(2/2) Solution : 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 Earn rewards if upload to other user Reward can be used in future Objective: Archive maximum cooperation Ex: KARMA Uses a single currency as a way to secure trading

Related work Reputation based system: 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 : provide download priority to user User with high score can download files from user with low scores

Related work VS. BitTorrent : BT doesn' t need any score, reputation,token simplicity BT-like model is less suitable for media streaming 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 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 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 : Object lookup Peer-based aggregated streaming Dynamic adaptations to network and peer conditions Quality of a peer depend on: Availability Offered rate capacity of outgoing link

P2P Streaming System Case Study Cope with fluctuation in network service: 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 : 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 90% of the frames are good 25% loss rate  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 Large populations Asymmetries of interest score-based incentive mechanism 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? 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: 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 Highest level represent ISP  Link delay: 100ms  Capacity varied from 1.5Mbps to 5Mbps second level stub domains  Link delay: 10ms  Link capacity: the same distribution as the transit domain 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 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

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  Reduces overhead in sending redundant data Future work: Add empirical data to refine incentive mechanism