A Payment-based Incentive and Service Differentiation Mechanism for P2P Streaming Broadcast

1. 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

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

3. Related Work Incentive & ServDiff Mechanisms for • 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) • …

4. 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

5. Payment-based auction model

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

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

8. 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. Advantage: Balanced and short tree (small loss rate) and simplicity. Disadvantage: No Nash Equilibrium.

9. 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. Advantage: Short tree and Nash Equilibrium. Disadvantage: Relatively complex.

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

11. In-Session Utility Maximization s2 s3 s1 b12=? b22=? b11=? b23=? b21=? C2 C1 b13=? 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).

12. In-Session Utility Maximization • uij: utility of substream j of peer i • Ui: collective utility of peer i • bij: bid price for substream j by peer i • Dij: mapping from bid price to data loss rate • Lij: mapping from bid price to substream latency • Ci: peer i’s total number of points (to be spent for bidding) Unknowns that need to be estimated!

13. In-Session Utility Maximization • 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 Problem solving by • Static even allocation strategy • Allocate points evenly to all substreams. • Advantage: simple. • Disadvantage: no Nash equilibrium. 4 6 5 5 5 4 5 2

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

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

16. 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. 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. Problem solving: • Estimate Ej(.) 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

17. Simulation: Effectiveness of Incentive Utility vs. Bandwidth Tree level number vs. Bandwidth

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

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

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

21. 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

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

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

24. Thank you!