Incentive-based Schemes
Smita Rai
ECS289L
Outline

Incentives for Co-operation in Peer-toPeer 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”
Incentives for Co-operation in Peer-toPeer 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.
Incentives for Co-operation in Peer-toPeer 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.
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.

Motivation
Many peer-to-peer systems rely on cooperation 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.

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”
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”.
Prisoner’s Dilemma






Suspect 2
Suspect 1

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.
Quiet
Fink
Quiet
1, 1
4, 0
Fink
0, 4
3, 3
Evolutionary Prisoner’s Dilemma
(EPD)

Enhancements
Repetition.
 Reputation.


Symmetric, the authors generalize it to
include asymmetric transactions (client –
server).
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.
Asymmetric EPD

General form of a Payoff Matrix
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.
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.
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.

Simulation results

Varying
Population sizes.
 Number of rounds.


Server
Payoff Matrix
Request
File
Client
Don’t
request file
Allow
Download
Ignore
Request
7, -1
0, 0
0,0
0,0
Results

Private vs. Shared History
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.
Results

Stranger Policies
100% Defect.
 100% Co-operate.
 Adaptive.

 Pc t+1


= (1- mu)* Pc t + mu * Ct
Ct = 1 if last stranger co-operated, 0 otherwise.
Pc t = probability to co-operate with stranger at time t.
Results
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.
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.

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.

Contents

Motivation
Critique of existing proposals.
 Benefits of the layered approach.

Priced Priority Forwarding.
 Simulation results.
 Conclusions.

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
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?
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.
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.
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.

Priority Forwarding


Tk = number of packets received in previous
epoch, at node k.
Each node receives payment for forwarding a
packet


Node k’s utility function:


mk = B Tk.
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).
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.
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.

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.

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.

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.
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.
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.
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.
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.
