A Payment-based Incentive and Service Differentiation Mechanism for P2P Streaming Broadcast Guang Tan and Stephen A. Jarvis Department of Computer Science,

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Presentation transcript:

A Payment-based Incentive and Service Differentiation Mechanism for P2P Streaming Broadcast Guang Tan and Stephen A. Jarvis Department of Computer Science, University of Warwick, United Kingdom June, 2006

Motivation Bandwidth-demanding Free-riding problem Goals Encourage contribution and discourage free-riding via ServDiff Achieve higher average media quality

Related Work P2P Streaming Related Applications Taxation model (Chu & Zhang. SIGCOMM-PINS’04) Score-based mechanism (Habib & Chuang. IWQoS’04) Pricing model (Wang & Li. INFOCOM’05) … General P2P Applications Score-based mechanism (Kazaa) Reputation systems (Kamvar et al. EigenTrust. WWW’03) Auction-based model (Semret et al. JSAC’00) … Incentive & ServDiff Mechanisms for

Payment-based auction model Multiple substreams Periods of fixed length (e.g., 3 min) Bidding for substream parents for next period in each period Payment in Points happens at the beginning of next period Bonus for serving zero-point peers A secure and efficient payment protocol (e.g., bank servers) An approximate time synchronization protocol (NTP) Assumptions

Payment-based auction model

Basic Protocol a) Peers submit bids to the root b) Highest bidders win the root c) Failed peers choose new targets from the winners and re-submit bids to the new targets d) Some peers win the new targets e) The same process continues until all peers have found their next-period parents, or they randomly find parents with best-effort Virtual overlay construction

Parent Selection Strategies (1) Shortest Path (SP) Strategy: A peer selects a parent from the candidates that makes the accumulated service latency the smallest. Disadvantage: A well located peer may attract most peers, resulting a highly unbalanced (tall) tree. Advantage: Small latency, simplicity

Parent Selection Strategies (2) Balanced Tree (BT) Strategy: A peer selects a candidate parent probabilistically. Given a set of candidates, the probability of one peer being picked is in proportional to its number of out slots. Disadvantage: No Nash Equilibrium. Advantage: Balanced and short tree (small loss rate) and simplicity.

Parent Selection Strategies (3) Shortest Path & Balanced Tree (SP-BT) Strategy: A peer first selects a parent using the SP strategy. If it fails to win a slot on that parent, it uses the BT strategy to select a parent. Disadvantage: Relatively complex. Advantage: Short tree and Nash Equilibrium.

Security Issue Block streaming! Non-incentive trees A fraction (e.g., 20%) of root slots for non- incentive service The non-incentive trees make the attack difficult The fraction of non-incentive root slots: tradeoff between incentive (thus performance) and security

In-Session Utility Maximization Purpose: Maximize the expected media quality in each period. Model: To find a best-reply in a game of incomplete information. Maximize the expected utility by planning bids for different substreams under the constraint of a certain number of points (earned in last period). C1 s1 s2s3 b11=? b12=? b13=? C2 b21=? b22=? b23=?

In-Session Utility Maximization u ij : utility of substream j of peer i U i : collective utility of peer i b ij : bid price for substream j by peer i D ij : mapping from bid price to data loss rate L ij : mapping from bid price to substream latency C i : peer i’s total number of points (to be spent for bidding) Unknowns that need to be estimated!

In-Session Utility Maximization Problem solving by History-based best-reply strategy Estimate Dij(.) and Lij(.) using a node’s own and others’ recent history information Solving for a good solution (i.e., the bid vector) using an approximate algorithm Disadvantages: Impossible to accurately estimate Dij(.) and Lij(.) due to unknown decisions by others and system dynamics Static even allocation strategy Allocate points evenly to all substreams. Advantage: simple. Disadvantage: no Nash equilibrium

Off-Session Point Accumulation Active mode (maximizing utility) Inactive mode (disconnected from the overlay) Half-active mode (maximizing wealth) Purpose: Maximize individual wealth in each period (and indirectly increase the system’s bandwidth supply).

Off-Session Point Accumulation Model: Maximize expected income in terms of points by buying service of some substreams and selling them to others. b j : bid price for substream j o i : #out slots for substream j b ij : bid price for substream j by peer i E j : mapping from bid price to expected income in terms of points W: total number of points O: total number of out slots E j (.) needs to be estimated!

Off-Session Point Accumulation Theorem: A peer can maximize its expected income in terms of points by buying a single (arbitrary) substream and selling that substream using all of its out slots. Problem solving: Estimate E j (.) using a peer’s own and others’ recent history information Solving for optimal solution (i.e., the bid for a substream) in O(W) time Implication: Since an off-session peer contributes all of its out slots while consuming only one slot from othters, the system’s bandwidth supply is increased.

Simulation: Effectiveness of Incentive Utility vs. BandwidthTree level number vs. Bandwidth

Simulation: Effectiveness of Incentive Average utility of all peers with and without incentive

Simulation: Effect of Period Length Incentive does not significantly increase protocol overheads because: 1.Period length in the order of minutes 2.Short tree

Simulation: Effect of Period Length The longer the period, the less chances the tree has to be optimized

Simulation: Parent Selection Strategies The effect of parent selection strategies on overall system performance depends on the factor of latency/loss rate in the utility

Simulation: Off-Session Point Accumulation Some typical peers’ wealth over time Change from utility maximization mode to point accumulation model (session ending time)

Simulation: Off-Session Point accumulation Simulation: Off-Session Point accumulation System’s resource increases as more peers choose to stay online and contribute after the normal session services.

Thank you!