Incentive-Based Schemes Smita

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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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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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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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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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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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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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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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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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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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.

Incentive-Based Schemes Smita

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