1. Incentive-based Schemes Smita Rai ECS289L

2. Outline Incentives for Co-operation in Peer-to- Peer Networks. Incentives for Co-operation in Peer-to- Peer Networks. Aimed at applications like file sharing. Aimed at applications like file sharing. Priority Forwarding in Ad hoc Networks with Self-Interested Parties. Priority Forwarding in Ad hoc Networks with Self-Interested Parties. Layered Incentive-based model for Ad hoc networks. 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 Kevin Lai Visiting Post -doctoral Researcher, UCB. Visiting Post -doctoral Researcher, UCB. PhD – Stanford. PhD – Stanford. Part of MosquitoNet group. Part of MosquitoNet group. Developed tools like Nettimer etc. Developed tools like Nettimer etc. Ion Stoica Ion Stoica Assistant Professor, UCB. Assistant Professor, UCB. PhD – CMU. PhD – CMU. Worked on a wide range of topics, one of them Incentives. Worked on a wide range of topics, one of them Incentives.

4. Incentives for Co-operation in Peer-to- Peer Networks Michal Feldman Michal Feldman PhD Student, UCB. PhD Student, UCB. John Chuang John Chuang Assistant Professor, UCB. Assistant Professor, UCB. PhD – CMU. PhD – CMU. All of them work on the OATH Project – Providing Incentives for Co-operation in P2P Systems. 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. Model of co-operation in P2P systems. Framework in terms of Evolutionary Prisoner’s Dilemma (EPD). Framework in terms of Evolutionary Prisoner’s Dilemma (EPD). Design space for possible incentive strategies. Design space for possible incentive strategies. Comparison using simulation. Comparison using simulation. Conclusions. Conclusions.

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

7. Tragedy of the Commons Coined by Garrett Hardin in Science, 1968. Coined by Garrett Hardin in Science, 1968. Pasture open to all. Pasture open to all. Herdsmen keeping cattle. Herdsmen keeping cattle. Rational herdsman wants to maximize his gains. Rational herdsman wants to maximize his gains. Add more cattle to his herd. Add more cattle to his herd. Positive component – The owner will get the gain. Positive component – The owner will get the gain. Negative component – The effects of overgrazing will be shared by all. Negative component – The effects of overgrazing will be shared by all. Result – “Freedom in a commons brings ruin to 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. Features of a model of co-operation in P2P systems. Universal co-operation leads to optimal overall utility. Universal co-operation leads to optimal overall utility. Individual incentive to defect. Individual incentive to defect. Rational behavior. Rational behavior. All these provide the essential tension that results in the tragedy of the commons. All these provide the essential tension that results in the tragedy of the commons. Authors look at incentive techniques to avoid this problem. Authors look at incentive techniques to avoid this problem. The specific application they look at is a file sharing system. 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”. 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. QuietFink Quiet 1, 1 4, 0 Fink 0, 4 3, 3 Suspect 2 Suspect 1

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

11. Asymmetric EPD AEPD consists of players who meet for games. AEPD consists of players who meet for games. A player can be a client in one game and a server in another. A player can be a client in one game and a server in another. The server has a choice between co-operation and defection. The server has a choice between co-operation and defection. Players decide depending on a strategy. Players decide depending on a strategy. They may maintain histories of other players’ actions. 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. 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 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. Round consists of one game by each player in the system as a client and a server. A generation consist of r rounds. A generation consist of r rounds. After a generation, all history is cleared. 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. 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 Reciprocative Decision function P(co-operation with X)= Min { P(co-operation with X)= Min { (Co-op X gave/ co-operation X received), 1} Private vs. Shared History Private vs. Shared History Private history does not scale to large population sizes. Private history does not scale to large population sizes. Repeat games become less likely with increase in population size. Repeat games become less likely with increase in population size. However, decentralized implementation straightforward. However, decentralized implementation straightforward.

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

16. Simulation results Varying Varying Population sizes. Population sizes. Number of rounds. Number of rounds. Payoff Matrix Payoff Matrix Allow Download Ignore Request Request File 7, -1 0, 0 Don’t request file 0,00,0 Server Client

17. Results Private vs. Shared History Private vs. Shared History

18. Results Convergence of Reciprocative using private history varies depending on Convergence of Reciprocative using private history varies depending on Population size. Population size. Initial mix of population. Initial mix of population. Rate at which players are making transactions. Rate at which players are making transactions. In any case, fails at some point as the population increases. 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. 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. So, a player using private history is taken advantage of by a defector.

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

20. Results

21. Conclusions Incentives techniques relying on private history fail as population size increases. 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. 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. 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. Appeared in Workshop on Economics of P2P Systems ’03, Berkeley. Barath Raghavan Barath Raghavan MS student at UCSD. MS student at UCSD. Alex C. Snoeren Alex C. Snoeren PhD, MIT. PhD, MIT. Assistant Professor, UCSD. Assistant Professor, UCSD. Several publications including IETF Documents. 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 Examines the problem of incentivizing autonomous self-interested nodes in an ad hoc network Proposes layered design Proposes layered design Policed but unpriced best-effort forwarding. Policed but unpriced best-effort forwarding. Priced priority forwarding. Priced priority forwarding.

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

25. Motivation Lack of co-operation can come in two flavors - Lack of co-operation can come in two flavors - Misbehavior – Nodes do not adhere to specifications of the protocol. 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. 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 Critique of the present schemes Assumption that all nodes use some fixed utility metric. Assumption that all nodes use some fixed utility metric. However, different nodes may have different tolerances for any particular 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. Single utility metric may lead to classification of alternatively motivated nodes as malicious. Scheme should not require global participation Scheme should not require global participation What about nodes which are incapable of participating? What about nodes which are incapable of participating?

27. Layered Design Benefits of separating the two Benefits of separating the two Nodes not well positioned to earn goodwill of others are not completely deprived of the service. 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. 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. 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. 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. Issue of centralized nodes for currency management, contrary to the spirit of ad hoc networks, left for future research. Goals: Goals: To ensure nodes that forward priority packets get reasonably compensated. To ensure nodes that forward priority packets get reasonably compensated. Nodes that do not forward packets in a priority fashion are unaffected. 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. Nodes with equal currency and similar topological locations receive similar improvements in delivery ratio.

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

30. Priority Forwarding T k = number of packets received in previous epoch, at node k. T k = number of packets received in previous epoch, at node k. Each node receives payment for forwarding a packet Each node receives payment for forwarding a packet m k = B T k. m k = B T k. Node k’s utility function: Node k’s utility function: u k = m k – c k, so B >= c k / T k u k = m k – c k, so B >= c k / T k Per-packet cost to send a priority packet from i to j along a given path p = Per-packet cost to send a priority packet from i to j along a given path p = Sum of m k for all nodes k along the path (excluding i and j). Sum of m k 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 m k from the currency previously attached to the packet. For each priority packet it forwards, node k takes a payment of m k 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). 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. To bootstrap, all nods start with some initial currency. Problem of price discovery Problem of price discovery Price discovery piggybacked on route requests. Price discovery piggybacked on route requests.

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

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

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

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

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

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

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