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8/8/2019 Incentive-Based Schemes Smita
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OutlineOutline
Incentives for Co-operation in Peer-to-PeerIncentives for Co-operation in Peer-to-Peer
Networks.Networks.
Aimed at applications like file sharing.Aimed at applications like file sharing.
Priority Forwarding in Ad hoc Networks with Self-Priority Forwarding in Ad hoc Networks with Self-Interested Parties.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-Provide incentives to self-interested users to co-
operateoperate
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Incentives for Co-operation in Peer-to-Incentives for Co-operation in Peer-to-
Peer NetworksPeer Networks
Kevin LaiKevin 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 StoicaIon Stoica Assistant Professor, UCB.Assistant Professor, UCB.
PhD CMU.PhD CMU.
Worked on a wide range of topics, one of themWorked on a wide range of topics, one of them
Incentives.Incentives.
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Incentives for Co-operation in Peer-to-Incentives for Co-operation in Peer-to-
Peer NetworksPeer Networks
Michal FeldmanMichal FeldmanPhD Student, UCB.PhD Student, UCB.
John ChuangJohn ChuangAssistant Professor, UCB.Assistant Professor, UCB.
PhD CMU.PhD CMU.
All of them work on the OATH Project All of them work on the OATH Project Providing Incentives for Co-operation in P2PProviding Incentives for Co-operation in P2PSystems.Systems.
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ContentsContents
Model of co-operation in P2P systems.Model of co-operation in P2P systems.
Framework in terms of EvolutionaryFramework in terms of Evolutionary
Prisoners Dilemma (EPD).Prisoners Dilemma (EPD).
Design space for possible incentiveDesign space for possible incentive
strategies.strategies.
Comparison using simulation.Comparison using simulation.Conclusions.Conclusions.
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MotivationMotivation
Many peer-to-peer systems rely on co-Many peer-to-peer systems rely on co-
operation among self-interested users.operation among self-interested users.
When non-cooperative users benefit fromWhen non-cooperative users benefit from
free riding on others resources Tragedyfree riding on others resources Tragedy
of the Commons.of the Commons.
Incentives for co-operation needed toIncentives for co-operation needed to
avoid this problem.avoid this problem.
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Tragedy of the CommonsTragedy 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 beNegative component The effects of overgrazing will be
shared by all.shared by all.
Result Freedom in a commons brings ruin toResult Freedom in a commons brings ruin to
allall
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Model of Co-operationModel 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 tragedyAll these provide the essential tension that results in the tragedyof the commons.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 thisThe approach is to model the problem of co-operation in thissystem in terms of Prisoners Dilemma.system in terms of Prisoners Dilemma.
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Prisoners DilemmaPrisoners Dilemma
Two suspects in a major crime areheld in separate cells.
There is enough evidence to convicteach of them of a minor offense.
Not enough evidence to convict eitherof them of the major crime.
If one of them acts as an informer
against the other (finks), then the othercan be convicted of the major crime.
If they both stay quiet, each will beconvicted of the minor offense andspend one year in prison.
If one and only one of them finks, shewill be freed, the other will spend fouryears in prison.
If they both fink, each will spend threeyears in prison.
Quiet Fink
Quiet 1, 1 4, 0
Fink 0, 4 3, 3
Suspect 2
Suspect
1
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Evolutionary Prisoners DilemmaEvolutionary Prisoners Dilemma
(EPD)(EPD)
EnhancementsEnhancementsRepetition.Repetition.
Reputation.Reputation.
Symmetric, the authors generalize it toSymmetric, the authors generalize it to
include asymmetric transactions (client include asymmetric transactions (client
server).server).
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Asymmetric EPDAsymmetric 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 aA player can be a client in one game and a
server in another.server in another. The server has a choice between co-operationThe server has a choice between co-operation
and defection.and defection. Players decide depending on a strategy.Players decide depending on a strategy. They may maintain histories of other playersThey may maintain histories of other players
actions.actions. As a result of client and servers actions, theAs a result of client and servers actions, the
payoffs from a payoff matrix are added to theirpayoffs from a payoff matrix are added to theirscores.scores.
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Asymmetric EPDAsymmetric EPD
General form of a Payoff MatrixGeneral form of a Payoff Matrix
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Design SpaceDesign Space
Reciprocative Decision functionReciprocative Decision function P(co-operation with X)= Min {P(co-operation with X)= Min {
(Co-op X gave/ co-operation X received), 1}(Co-op X gave/ co-operation X received), 1}
Private vs. Shared HistoryPrivate vs. Shared History Private history does not scale to large populationPrivate history does not scale to large population
sizes.sizes.
Repeat games become less likely with increase inRepeat games become less likely with increase in
population size.population size. However, decentralized implementationHowever, decentralized implementation
straightforward.straightforward.
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Design SpaceDesign Space
Policy with strangersPolicy with strangersLegitimate newcomer.Legitimate newcomer.Whitewasher.Whitewasher.
Authors assume that the P2P systemsAuthors assume that the P2P systemsthey model, have zero cost identitiesthey model, have zero cost identities
Objective vs. Subjective reputationObjective vs. Subjective reputation
Objective reputation may be subverted byObjective reputation may be subverted bycollusion.collusion.
Subjective reputation can avoid this problem.Subjective reputation can avoid this problem.
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Simulation resultsSimulation results
VaryingVaryingPopulation sizes.Population sizes.
Number of rounds.Number of rounds.
Payoff MatrixPayoff Matrix AllowDownloadIgnore
Request
Request File
7, -1 0, 0
Dontrequest file
0,0 0,0
Server
Client
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ResultsResults
Private vs. Shared HistoryPrivate vs. Shared History
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ResultsResults
Private vs. Shared HistoryPrivate vs. Shared History Convergence of Reciprocative using private historyConvergence of Reciprocative using private history
varies depending onvaries 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 sameSince it is less likely that you have repeat games with the same
player.player. So, a player using private history is taken advantage of by aSo, a player using private history is taken advantage of by a
defector.defector.
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ResultsResults
Stranger PoliciesStranger Policies100% Defect.100% Defect.
100% Co-operate.100% Co-operate.
Adaptive.Adaptive.
PPcct+1t+1 = (1- mu)* P= (1- mu)* P
cctt + mu * C+ mu * C
tt
CCtt= 1 if last stranger co-operated, 0 otherwise.= 1 if last stranger co-operated, 0 otherwise.
PPcctt
= probability to co-operate with stranger at time t.= probability to co-operate with stranger at time t.
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ResultsResults
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ConclusionsConclusions
Incentives techniques relying on private historyIncentives techniques relying on private historyfail as population size increases.fail as population size increases.
Shared history scales to large populations butShared history scales to large populations but
requires supporting infrastructure and isrequires supporting infrastructure and isvulnerable to collusion.vulnerable to collusion. Incentive techniques that adapt to the behaviorIncentive techniques that adapt to the behavior
of strangers can cause systems to converge toof strangers can cause systems to converge to
complete co-operation, despite no centralizedcomplete co-operation, despite no centralizedidentity allocation.identity allocation.
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Priority Forwarding in Ad hoc Networks withPriority Forwarding in Ad hoc Networks with
Self-Interested PartiesSelf-Interested Parties
Appeared in Workshop on Economics ofAppeared in Workshop on Economics ofP2P Systems 03, Berkeley.P2P Systems 03, Berkeley.
Barath RaghavanBarath RaghavanMS student at UCSD.MS student at UCSD.
Alex C. SnoerenAlex C. SnoerenPhD, MIT.PhD, MIT.
Assistant Professor, UCSD.Assistant Professor, UCSD.Several publications including IETFSeveral publications including IETF
Documents.Documents.
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Priority Forwarding in Ad hoc Networks withPriority Forwarding in Ad hoc Networks with
Self-Interested PartiesSelf-Interested Parties
Examines the problem of incentivizingExamines the problem of incentivizing
autonomous self-interested nodes in an adautonomous self-interested nodes in an ad
hoc networkhoc network
Proposes layered designProposes layered designPoliced but unpriced best-effort forwarding.Policed but unpriced best-effort forwarding.
Priced priority forwarding.Priced priority forwarding.
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ContentsContents
MotivationMotivationCritique 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.
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MotivationMotivation
Lack of co-operation can come in twoLack of co-operation can come in two
flavors -flavors -Misbehavior Nodes do not adhere toMisbehavior Nodes do not adhere to
specifications of the protocol.specifications of the protocol.
Greed Nodes operate in a manner toGreed Nodes operate in a manner to
optimize a particular local utility function,optimize a particular local utility function,
possibly at the expense of other nodes.possibly at the expense of other nodes.Not necessarily distinct, but do not subsumeNot necessarily distinct, but do not subsume
each othereach other
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MotivationMotivation
Critique of the present schemesCritique of the present schemesAssumption that all nodes use some fixedAssumption that all nodes use some fixed
utility metric.utility metric.
However, different nodes may have differentHowever, different nodes may have differenttolerances for any particular metric.tolerances for any particular metric.
Single utility metric may lead to classification ofSingle utility metric may lead to classification ofalternatively motivated nodes as malicious.alternatively motivated nodes as malicious.
Scheme should not require globalScheme should not require globalparticipationparticipationWhat about nodes which are incapable ofWhat about nodes which are incapable of
participating?participating?
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Layered DesignLayered Design
Benefits of separating the twoBenefits of separating the two Nodes not well positioned to earn goodwill of othersNodes not well positioned to earn goodwill of others
are not completely deprived of the service.are not completely deprived of the service.
Incentive based priority forwarding can effectivelyIncentive based priority forwarding can effectivelymoderate the behavior of self-interested nodes.moderate the behavior of self-interested nodes.
Existence of a policed best-effort service may obviateExistence of a policed best-effort service may obviate
out-of-band communication channels to implementout-of-band communication channels to implement
virtual currency, enabling the deployment of proposedvirtual currency, enabling the deployment of proposed
incentive-base schemes.incentive-base schemes.
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Priority ForwardingPriority Forwarding
Relies on the existence of secure virtual currency.Relies on the existence of secure virtual currency. Issue of centralized nodes for currency management,Issue of centralized nodes for currency management,
contrary to the spirit of ad hoc networks, left for futurecontrary to the spirit of ad hoc networks, left for futureresearch.research.
Goals:Goals: To ensure nodes that forward priority packets get reasonablyTo ensure nodes that forward priority packets get reasonably
compensated.compensated. Nodes that do not forward packets in a priority fashion areNodes that do not forward packets in a priority fashion are
unaffected.unaffected.
Nodes with equal currency and similar topological locationsNodes with equal currency and similar topological locationsreceive similar improvements in delivery ratio.receive similar improvements in delivery ratio.
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Priority ForwardingPriority Forwarding
The protocol prices priority forwarding.The protocol prices priority forwarding.
Nodes pay a price per packet based onNodes pay a price per packet based on
the traffic along the forwarding path.the traffic along the forwarding path.
Prices change only at epoch boundaries.Prices change only at epoch boundaries.
Intrinsic cost of priority forwarding at nodeIntrinsic cost of priority forwarding at node
k = ck = ckk, c, ckk = 0 for nodes not supporting= 0 for nodes not supportingpriority forwarding.priority forwarding.
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Priority ForwardingPriority Forwarding
TTkk= number of packets received in previous= number of packets received in previous
epoch, at node k.epoch, at node k. Each node receives payment for forwarding aEach node receives payment for forwarding a
packetpacketmmkk = B T= B Tkk..
Node ks utility function:Node ks utility function: uu
kk= m= m
kk c c
kk, so B >= c, so B >= c
kk/ T/ T
kk
Per-packet cost to send a priority packet from iPer-packet cost to send a priority packet from ito j along a given path p =to j along a given path p = Sum of mSum of m
kkfor all nodes k along the path (excluding ifor all nodes k along the path (excluding i
and j).and j).
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Priority ForwardingPriority Forwarding
For each priority packet it forwards, node k takesFor each priority packet it forwards, node k takesa payment of ma payment of m
kkfrom the currency previouslyfrom the currency previously
attached to the packet.attached to the packet. In order to earn this payment, node k must sendIn order to earn this payment, node k must send
this packet as priority over any best-effort trafficthis packet as priority over any best-effort traffic(enforced by the next hop node promiscuously(enforced by the next hop node promiscuouslyobserving ks transmissions).observing ks transmissions).
To bootstrap, all nods start with some initialTo bootstrap, all nods start with some initial
currency.currency. Problem of price discoveryProblem of price discovery Price discovery piggybacked on route requests.Price discovery piggybacked on route requests.
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Priority forwardingPriority forwarding
Authors claim their pricing scheme satisfiesAuthors claim their pricing scheme satisfiesstandard pricing stability requirements.standard pricing stability requirements.
Use simulation results to show that theirUse simulation results to show that their
model provides:model provides:Fairness (Currency must provide equal value toFairness (Currency must provide equal value to
all similarly situated nodes).all similarly situated nodes).Marginal utility.Marginal utility.Partial deployment.Partial deployment.
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SimulationSimulation
Fixed topology.Fixed topology.
Routing conducted using AODV protocol.Routing conducted using AODV protocol.
Route requests forwarded as priority butRoute requests forwarded as priority butignored by the pricing system.ignored by the pricing system.
Nodes prices calculated every second.Nodes prices calculated every second.
Simulates 200 seconds of packetSimulates 200 seconds of packettransmissions.transmissions.
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Simulation ResultsSimulation Results
Pricing fairnessPricing fairness Improvement in delivery ratio obtained byImprovement in delivery ratio obtained by
spending any fixed amount of currency,spending any fixed amount of currency,
should be same across all similarly situatedshould be same across all similarly situatednodes.nodes.
Nodes send their traffic as priority wheneverNodes send their traffic as priority whenever
money is available, and resort to best-effortmoney is available, and resort to best-effort
otherwise.otherwise.
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Simulation ResultsSimulation Results
Simulated networkSimulated network Symmetric alongSymmetric along
several axes.several axes. Nodes 1 and 7 areNodes 1 and 7 are
similarly situated.similarly situated. They receive equalThey receive equal
currency.currency. Nodes 0-7 act asNodes 0-7 act as
sources.sources. Nodes 8-15 sink traffic.Nodes 8-15 sink traffic. Node 16 only forwards.Node 16 only forwards.
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Simulation ResultsSimulation Results
Both nodes haveBoth nodes havesimilar trends forsimilar trends forincrease in deliveryincrease in deliveryratios.ratios.
The nodes turn onThe nodes turn onand off prioritizationand off prioritizationas they earn moneyas they earn moneyand spend it.and spend it.
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Simulation ResultsSimulation Results
Marginal UtilityMarginal Utility Provides different levelsProvides different levels
of service with differentof service with differentinitial currencies.initial currencies.
Nodes 1, 5, 7 areNodes 1, 5, 7 aresimilarly situated butsimilarly situated butreceive roughly linearlyreceive roughly linearlydecreasing currency.decreasing currency.
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Simulation ResultsSimulation Results
Partial deploymentPartial deployment To prove the feasibilityTo prove the feasibility
of partial deployment.of partial deployment. Serves as an argumentServes as an argument
to layered approach.to layered approach. Node 2 sends priorityNode 2 sends priority
traffic with two degreestraffic with two degreesof partial deployment:of partial deployment:
2 centrally located nodes2 centrally located nodesdont participate.dont participate. 8 centrally located nodes8 centrally located nodes
dont participate.dont participate.
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ConclusionConclusion
A priced priority forwarding scheme builtA priced priority forwarding scheme built
upon a policed best-effort forwardingupon a policed best-effort forwarding
system affords more flexibility with respectsystem affords more flexibility with respect
to heterogeneous user population.to heterogeneous user population.Still enables service differentiation andStill enables service differentiation and
various degrees of fairness.various degrees of fairness.