Incentive-based Schemes

1. Incentive-based Schemes Smita Rai ECS289L

2. Outline • Incentives for Co-operation in Peer-to-Peer Networks. • Aimed at applications like file sharing. • Priority Forwarding in Ad hoc Networks with Self-Interested Parties. • Layered Incentive-based model for Ad hoc networks. “Provide incentives to self-interested users to co-operate”

3. Incentives for Co-operation in Peer-to-Peer Networks • Kevin Lai • Visiting Post -doctoral Researcher, UCB. • PhD – Stanford. • Part of MosquitoNet group. • Developed tools like Nettimer etc. • Ion Stoica • Assistant Professor, UCB. • PhD – CMU. • Worked on a wide range of topics, one of them Incentives.

4. Incentives for Co-operation in Peer-to-Peer Networks • Michal Feldman • PhD Student, UCB. • John Chuang • Assistant Professor, UCB. • PhD – CMU. All of them work on the OATH Project – Providing Incentives for Co-operation in P2P Systems.

5. Contents • Model of co-operation in P2P systems. • Framework in terms of Evolutionary Prisoner’s Dilemma (EPD). • Design space for possible incentive strategies. • Comparison using simulation. • Conclusions.

6. Motivation • Many peer-to-peer systems rely on co-operation among self-interested users. • When non-cooperative users benefit from free riding on others’ resources – “Tragedy of the Commons”. • Incentives for co-operation needed to avoid this problem.

7. Tragedy of the Commons • Coined by Garrett Hardin in Science, 1968. • Pasture open to all. • Herdsmen keeping cattle. • Rational herdsman wants to maximize his gains. • Add more cattle to his herd. • Positive component – The owner will get the gain. • Negative component – The effects of overgrazing will be shared by all. • Result – “Freedom in a commons brings ruin to all”

8. Model of Co-operation • Features of a model of co-operation in P2P systems. • Universal co-operation leads to optimal overall utility. • Individual incentive to defect. • Rational behavior. • All these provide the essential tension that results in the tragedy of the commons. • Authors look at incentive techniques to avoid this problem. • The specific application they look at is a file sharing system. • The approach is to model the problem of co-operation in this system in terms of “Prisoners’ Dilemma”.

9. Prisoner’s Dilemma • Two suspects in a major crime are held in separate cells. • There is enough evidence to convict each of them of a minor offense. • Not enough evidence to convict either of them of the major crime. • If one of them acts as an informer against the other (finks), then the other can be convicted of the major crime. • If they both stay quiet, each will be convicted of the minor offense and spend one year in prison. • If one and only one of them finks, she will be freed, the other will spend four years in prison. • If they both fink, each will spend three years in prison. Suspect 2 Suspect 1

10. Evolutionary Prisoner’s Dilemma (EPD) • Enhancements • Repetition. • Reputation. • Symmetric, the authors generalize it to include asymmetric transactions (client – server).

11. Asymmetric EPD • AEPD consists of players who meet for games. • A player can be a client in one game and a server in another. • The server has a choice between co-operation and defection. • Players decide depending on a strategy. • They may maintain histories of other players’ actions. • As a result of client and server’s actions, the payoffs from a payoff matrix are added to their scores.

12. Asymmetric EPD • General form of a Payoff Matrix

13. Asymmetric EPD • Round consists of one game by each player in the system as a client and a server. • A generation consist of r rounds. • After a generation, all history is cleared. • Players evolve from their current strategies to higher scoring strategies in proportion to the difference between the average scores of the two strategies, after a generation.

14. Design Space • Reciprocative Decision function • P(co-operation with X)= Min { (Co-op X gave/ co-operation X received), 1} • Private vs. Shared History • Private history does not scale to large population sizes. • Repeat games become less likely with increase in population size. • However, decentralized implementation straightforward.

15. Design Space • Policy with strangers • Legitimate newcomer. • Whitewasher. Authors assume that the P2P systems they model, have zero cost identities • Objective vs. Subjective reputation • Objective reputation may be subverted by collusion. • Subjective reputation can avoid this problem.

16. Simulation results • Varying • Population sizes. • Number of rounds. • Payoff Matrix Server Client

17. Results • Private vs. Shared History

18. Results • Private vs. Shared History • Convergence of Reciprocative using private history varies depending on • Population size. • Initial mix of population. • Rate at which players are making transactions. • In any case, fails at some point as the population increases. • Since it is less likely that you have repeat games with the same player. • So, a player using private history is taken advantage of by a defector.

19. Results • Stranger Policies • 100% Defect. • 100% Co-operate. • Adaptive. • Pct+1 = (1- mu)* Pct + mu * Ct • Ct = 1 if last stranger co-operated, 0 otherwise. • Pct = probability to co-operate with stranger at time t.

20. Results

21. Conclusions • Incentives techniques relying on private history fail as population size increases. • Shared history scales to large populations but requires supporting infrastructure and is vulnerable to collusion. • Incentive techniques that adapt to the behavior of strangers can cause systems to converge to complete co-operation, despite no centralized identity allocation.

22. Priority Forwarding in Ad hoc Networks with Self-Interested Parties • Appeared in Workshop on Economics of P2P Systems ’03, Berkeley. • Barath Raghavan • MS student at UCSD. • Alex C. Snoeren • PhD, MIT. • Assistant Professor, UCSD. • Several publications including IETF Documents.

23. Priority Forwarding in Ad hoc Networks with Self-Interested Parties • Examines the problem of incentivizing autonomous self-interested nodes in an ad hoc network • Proposes layered design • Policed but unpriced best-effort forwarding. • Priced priority forwarding.

24. Contents • Motivation • Critique of existing proposals. • Benefits of the layered approach. • Priced Priority Forwarding. • Simulation results. • Conclusions.

25. Motivation • Lack of co-operation can come in two flavors - • Misbehavior – Nodes do not adhere to specifications of the protocol. • Greed – Nodes operate in a manner to optimize a particular local utility function, possibly at the expense of other nodes. Not necessarily distinct, but do not subsume each other

26. Motivation • Critique of the present schemes • Assumption that all nodes use some fixed utility metric. • However, different nodes may have different tolerances for any particular metric. • Single utility metric may lead to classification of alternatively motivated nodes as malicious. • Scheme should not require global participation • What about nodes which are incapable of participating?

27. Layered Design • Benefits of separating the two • Nodes not well positioned to earn goodwill of others are not completely deprived of the service. • Incentive based priority forwarding can effectively moderate the behavior of self-interested nodes. • Existence of a policed best-effort service may obviate out-of-band communication channels to implement virtual currency, enabling the deployment of proposed incentive-base schemes.

28. Priority Forwarding • Relies on the existence of secure virtual currency. • Issue of centralized nodes for currency management, contrary to the spirit of ad hoc networks, left for future research. • Goals: • To ensure nodes that forward priority packets get reasonably compensated. • Nodes that do not forward packets in a priority fashion are unaffected. • Nodes with equal currency and similar topological locations receive similar improvements in delivery ratio.

29. Priority Forwarding • The protocol prices priority forwarding. • Nodes pay a price per packet based on the traffic along the forwarding path. • Prices change only at “epoch” boundaries. • Intrinsic cost of priority forwarding at node k = ck, ck = 0 for nodes not supporting priority forwarding.

30. Priority Forwarding • Tk = number of packets received in previous epoch, at node k. • Each node receives payment for forwarding a packet • mk = B Tk. • Node k’s utility function: • uk = mk – ck, so B >= ck / Tk • Per-packet cost to send a priority packet from i to j along a given path p = • Sum of mk for all nodes k along the path (excluding i and j).

31. Priority Forwarding • For each priority packet it forwards, node k takes a payment of mk from the currency previously attached to the packet. • In order to earn this payment, node k must send this packet as priority over any best-effort traffic (enforced by the next hop node promiscuously observing k’s transmissions). • To bootstrap, all nods start with some initial currency. • Problem of price discovery • Price discovery piggybacked on route requests.

32. Priority forwarding • Authors claim their pricing scheme satisfies standard pricing stability requirements. • Use simulation results to show that their model provides: • Fairness (Currency must provide equal value to all similarly situated nodes). • Marginal utility. • Partial deployment.

33. Simulation • Fixed topology. • Routing conducted using AODV protocol. • Route requests forwarded as priority but ignored by the pricing system. • Nodes prices calculated every second. • Simulates 200 seconds of packet transmissions.

34. Simulation Results • Pricing fairness • Improvement in delivery ratio obtained by spending any fixed amount of currency, should be same across all similarly situated nodes. • Nodes send their traffic as priority whenever money is available, and resort to best-effort otherwise.

35. Simulation Results • Simulated network • Symmetric along several axes. • Nodes 1 and 7 are similarly situated. • They receive equal currency. • Nodes 0-7 act as sources. • Nodes 8-15 sink traffic. • Node 16 only forwards.

36. Simulation Results • Both nodes have similar trends for increase in delivery ratios. • The nodes turn on and off prioritization as they earn money and spend it.

37. Simulation Results • Marginal Utility • Provides different levels of service with different initial currencies. • Nodes 1, 5, 7 are similarly situated but receive roughly linearly decreasing currency.

38. Simulation Results • Partial deployment • To prove the feasibility of partial deployment. • Serves as an argument to layered approach. • Node 2 sends priority traffic with two degrees of partial deployment: • 2 centrally located nodes don’t participate. • 8 centrally located nodes don’t participate.

39. Conclusion • A priced priority forwarding scheme built upon a policed best-effort forwarding system affords more flexibility with respect to heterogeneous user population. • Still enables service differentiation and various degrees of fairness.