Incentive for P2P Fair Resource Sharing

Transcription

1 Incentive for P2P Fair Resource Sharing Emmanuelle Anceaume CNRS IRISA, France Joint work with Maria Gradinariu (IRISA), Aina Ravoaja (IRISA)

2 Facing Rationality Classical distributed systems P2P Either obedient or malicious May be neither obedient, nor malicious just rational Facing rationality [Shneidman & Parkes] Ignore it and hope that the system does its best Limit its impact by using trusted mechanisms Adopt fault tolerance techniques None of these approaches benefit from resources that may be potentially offered by all the nodes of the system Consider designing networks with self-interest in mind

3 Problem Designing a mechanism to optimize the resource sharing in P2P systems and proposing an architecture that implements such a mechanism

4 Model of the System Communication Model Large finite yet unbounded set of nodes (users) Nodes are uniquely identified Users can join, leave at any time Communication is done by message passing Failure Model Nodes can suffer from transient and permanent failures Best effort datagram service

5 Model of the System (cont.) Node Behavior Nodes are rational because they want to maximize their utility Nodes are strategic because they can choose the actions That minimize their participation (free-riding strategy) That maximize their access level (greedy strategy) Fairness = nodes are neither free-rider nor greedy U(p,t)= b(p,r,t ) al(p,t ) - c(p,r) part(p,r,t) b(p,r,t) = benefit that p gains by using r at t al(p,t) = fraction of resource p has access to at t c(p,r) = cost induced to p for each unit of its accessed resource r part(p,r,t) = quantity of resource that p has offered until t

6 The Fair Resource Sharing Problem Request Sovereignty Eventually, any request sent by a non-free rider is satisfied if the requested resource is available in the system Peer Sovereignty Any peer is allowed to request a resource infinitely often Peer Fairness If a request is sent infinitely often then it is received infinitely often by any peer that matches it Peer Cooperation If a non-free rider receives infinitely often a matching request then it satisfies it infinitely often

7 Principles of our Solution To motivate nodes to cooperate, we extend the classical differential service to a fair differential service, the fair cooperation incentive Differential service Increase the access level according to the participation Trade-off between access level and participation Encourages selfishness: increase popularity of some nodes vs newcomers Fair differential service Motivates peers to forward requests to less solicited ones Ensures that whenever a peer behaves as required it gains full access to the system resources while whenever it changes its strategy, its access level is changed accordingly.

8 Principles of our Solution (cont.) Evaluation of node s behavior at joining time and when he is detected free-rider (i.e. his suspicion level reaches an upper bound susp_max) Sending several requests in the raw to the node and computing his access level Senders are chosen within the semantic neighborhood of the tested node Neither senders nor tested node are aware of the evaluation test Socially beneficial Motivate nodes to change their strategy quickly al(t,p) = al 0 +(1-al 0 )(2part(p,r,R)-R)/R if part(p,r,r) 0, ε otherwise

9 Principles of our Solution (cont.) Cooperative Strategy Satisfaction of a request according to the participation level within his neighborhood Fair Strategy µ-request-acceptance Rule (R1): q accepts a request from p for resource r with probability f q (p,t) if for all q s neighbors s, part(q,r,t) -part(s,r,t) µ By setting f q (p,t) to al(p,t) each node has access to a fraction al(p,t) of the system resources When a node considers himself busy enough, it forwards the received request to a less solicited neighbor µ-request-acceptance Rule (R2): q forwards a request for resource r to node s with probability f q (s,t) if part(q,r,t) -part(s,r,t) µ Benefits to newcomers Fair cooperative Node: A node is fair cooperative if upon receipt of a request it executes either Rule 1 or Rule 2 Free-rider = non-fair cooperative node

10 Principles of our Solution (cont.) Our mechanism is characterized by the following three properties: Cooperative peers rewarding property Eventually, the expected access level of a fair cooperative peer equals 1 Non-cooperative peer discrimination property: Eventually, the expected access level of a non-cooperative peer equals ε Fairness property: Eventually, the participation level of any two fair cooperative peers are no more than µ apart

11 Architecture to implement incentives Registration service: assigns supervisor(s) to nodes Semantic group membership service: self-organizes nodes into semantic groups Cooperation tracking service: tracks node suspicion level Aggregation service: combines information to compute access and participation levels

12 Some Simulations 1000 nodes 10,000 time units Each node makes 1 request per 10 time units 50% of obedient nodes 10% of free-riders 40% of adaptive nodes Access Level

13 Some Simulations 1000 nodes 10,000 time units Each node makes 1 request per 10 time units 50% of obedient nodes 10% of free-riders 40% of adaptive nodes Variance of the participation level

14 Conclusion and Future Work Proposed an incentive mechanism for fair resource sharing Architecture that implements our mechanism (aggregation service, semantic group membership, tracking) Analysis of our mechanism Extending the simulation work to measure the trade-off between the fairness in the load balancing and the request serving efficiency Focus on the supervising overlay

15 Thank you!