Yoni Deny Confess Hadas Deny Redo the test Yoni is free Hadas is expelled from school Confess Yoni is expelled from school Hadas is free Both fail this course Yoni Deny ID: 809249
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Slide1
P2P Incentives
Dror
Marcus
Slide2Yoni
Deny
Confess
Hadas
Deny
Redo
the test
Yoni is
free
Hadas
is expelled from school
Confess
Yoni is
expelled
from school
Hadas
is free
Both fail
this course
Slide3Yoni
Deny
Confess
Hadas
Deny
Hadas : 2Yoni : 2Hadas : 0Yoni : 3Confess Hadas : 3Yoni : 0Hadas : 1Yoni : 1
Nash Equilibrium
Slide4Outline
Optimizing Scrip Systems[1][2]
Modeling the problem
Finding optimal solutions
One hop reputation for
BitTorrent[3]Improving Tit-For-Tat
Slide5Optimizing Scrip Systems
What is Scrip
?
Non-governmental currency
Users pay other users for service with scrip
Free riding is prevented through the need to earn scrip
Slide6Optimizing Scrip Systems
How to model the problem?
Determine the amount of money in the system
How to set the pricing in the systems
How do newcomers effect the system
Slide7Optimizing Scrip Systems:
Model
Game of rounds
Each agent
i
: a: cost of satisfying a request b: probability of being able to satisfy a request g: value of having a request satisfied d: discount rate r: relative request rate (cost $1 if granted) : agent i‘s utility in round rTotal utility of an agent:
Slide8Optimizing Scrip Systems:
initial
i
nteresting result
Altruism & Hoarders
There exists an value C, that depends only on a, b and d such that, in G(n, d, b, a) with at least C altruists, not volunteering is a dominant strategySet C > log1-b (a (1-d))Player’s request get satisfied for free with prob. Most additional expected utility to gain by having money is:
Slide9Optimizing Scrip Systems:
initial
i
nteresting result - cont
Example:
b = 0.01 (each player can satisfy 1% of requests)a = 0.1d = 0.9999/day (≈ 0.95 per year)r = 1 per day Only need C > 1145Therefore, adding reputation system on top of existing P2P systems to influence cooperation will have no effect on rational users!Is this a problem?
Slide10Optimizing Scrip Systems:
Model Revised
Game of rounds
An
agent’s type t = (
at,bt,gt,dt,rt) at: cost of satisfying a request bt: probability of being able to satisfy a request gt: value of having a request satisfied dt: discount rate rt: relative request rate (probability to need something)
Slide11Optimizing Scrip Systems:
Strategy
Picking a wining strategy for players
Threshold strategy to win
S
kShow that this is the best strategyThreshold StrategyIn some round, I have k dollars and have to decide whether to volunteer. What should I do?Sk: Volunteer if I have less than k dollarsk is your “comfort level,” how much you want to have saved up for future requestsS0 corresponds to never volunteering and S¥ corresponds to always volunteering
Slide12Optimizing Scrip Systems:
Playing the game
Examining the game
How to describe the state of the system?
Slide13Optimizing Scrip Systems:
Playing the game
How to describe the state of the system?
Look at the distribution of Money.
Each state in the system represents the amount of money each player has.
Specifically:Each player can have {0,….,k} dollars ($)Let Dk denote the set of probability distributions on {0,…,k} represents the fraction of people that have each amount of moneyEach state s has its own distribution
Slide14Optimizing Scrip Systems:
Playing the game
The system is analyzed as a Markov Chain
The system always ends up in the same place!
Markov Chain
≈ The probability of moving from the current state to another only depends on the current state of the system. (We don’t care about the past)
Slide15Optimizing Scrip Systems:
Playing the game
The system is analyzed as a Markov Chain
The system always ends up in the same place*
Slide16Optimizing Scrip Systems:
Reaching Equilibrium
Equilibrium exists
There is an efficient algorithm to find this
equilibrium
Agent response : How to set its threshold (k)If dt is not too small, and every agent but i plays a threshold strategy, then agent i has an e-best response that is a threshold strategy. Agent i’s best response function is monotone in the strategies of the other agents
Slide17Optimizing Scrip Systems:
Reaching
Equilibrium
Slide18Optimizing Scrip Systems:
Recap
Have a model to describe the system
System reaches a steady state (money distribution)
Equilibrium exists
What Next?
Slide19Optimizing Scrip Systems:
optimize performance
Improved performance
Better social welfare i.e.
M
ore utilityHow to improve performance?Increasing the amount of money in the system up to a certain point, after which the system experience a monetary crash The Capitol Hill Babysitting Co-op
Slide20Optimizing Scrip Systems:
optimize performance
The Capitol Hill Babysitting Co-op
Issued supply of scrip: each coupon work 30min babysitting.
Initially all saved for a rainy day
More coupons were issuedMost couples felt “rich”, no one wanted to babysit
Slide21Optimizing Scrip Systems:
optimize
performance
Given an Equilibrium, how good is it?
If a request in satisfied, social welfare increases by
gt-at.What are the chances for a request being satisfied:Requester needs to have money – Probability 1-M0Need to have a volunteer – Probability ≈1 Total expected welfare summed over all rounds: Therefore minimizing M0 maximizes utility!
Slide22Optimizing Scrip Systems:
optimize
performance
Given an Equilibrium, how good is it?
If a request in satisfied, social welfare increases by
gt-at.What are the chances for a request being satisfied:Requester needs to have money – Probability 1-M0Need to have a volunteer – Probability ≈1 Total expected welfare summed over all rounds: Therefore minimizing M0 maximizes utility!HW? – How did we reach this equation?
Slide23Optimizing Scrip Systems:
optimize
performance
Slide24Outline
Optimizing Scrip Systems[1][2]
Modeling the problem
Finding optimal solutions
One hop reputation for
BitTorrent[3]Improving Tit-For-TatIncentives for Live P2P streaming (bonus )Creating incentives to stream video
Slide25BitTorrent
Refresh
File is broken to small blocks (32-256 KB
)
A
tracker site keeps track of the active participants + extra statistics. Seed – A peer that finished downloading, and has all the pieces of the fileLeecher – A peer that is still downloading pieces of the fileSwarm – tracker sends list of ~40 peers downloading the file to a new leecher together forming a swarm.Chocking Algorithm – Choose top k-1 peers from the swarm with highest rates + 1 random peer (optimistic unchoke) to upload to. All k peers receive the same upload rate.
Slide26BitTorrent
Refresh
File is broken to small blocks (32-256 KB
)
A
tracker site keeps track of the active participants + extra statistics. Seed – A peer that finished downloading, and has all the pieces of the fileLeecher – A peer that is still downloading pieces of the fileSwarm – tracker sends list of ~40 peers downloading the file to a new leecher together forming a swarm.Chocking Algorithm – Choose top k-1 peers from the swarm with highest rates + 1 random peer (optimistic unchoke) to upload to. All k peers receive the same upload rate.HW – Think of at least two different ways to exploit the current incentives methods in Bittorrent (tit-for-tat, chocking, unchocking) to gain more download rate for less upload
Slide27One hop reputation :
Sharing in the wiled
Large scale measurements of
bittorent
“in the wild”
More then 14 million peers and 60,000 swarms studied.Data was collected in two traces BT-1 (~13K swarms) and BT-2 (~55K Swarms).Measurements were used to analyze both current BitTorrent performance and the new “one-hop” method effectiveness.
Slide28One hop reputation :
BitTorrent
Performance
Performance and availability are extremely poor
Median download rate in swarms is 14
KBps for a peer contributing 100KBps.25% of swarms are unavailable.
Slide29One hop reputation :
BitTorrent
Performance - cont
Weak Incentives
Peers at low end of capacity see large returns in
contributionsIncrease by 100-fold only improves by 2-foldThe incentive is the weakest for the peers that can help the most
Slide30One hop reputation :
BitTorrent
Performance - cont
Altruism
Seeds do not have requests and can’t use tit-for-tat for making decisions.
Too many seeds weakens contribution incentives.Too few seeds also weakens incentives since peers quickly run out of data to trade.Overall performance is mixedSome swarms enjoy many altruistic donations (enabling free-riding).Other swarms are “starved” for data
Slide31One hop reputation:
What can be done?
Can long-term history between peers be used (instead of tit-for-tat)?
Slide32One hop reputation:
What can be done?
Can long-term history between peers be used (instead of tit-for-tat)?
Slide33One hop reputation
97% of all peers observed in trace BT-2 are connected directly or through one of the 2000 most popular peers.
Slide34One hop reputation
Protocol
Notations and peer state information
Slide35One hop reputation
Protocol
Each peer selects it’s
top K
intermediaries.
When two peers meet first, they exchange top K sets and compute intersection.For each intermediate, peers request receipts of contributions.Multiple peers can serve as intermediaries.Receipt messages are sent periodically from receiver to sender. Also update messages are sent to intermediaries.
Slide36For each peer P request
If P interacted with me before use:
Otherwise get random subset of joint Top K mediators and compute reputation according to:
Where:
One hop
reputationDefault policy
Slide37For each peer P request
If reputation is above a threshold, send data to P at rate proportional to it’s reputation
One hop
reputation
Default policy
- cont
Slide38One hop reputation
Default policy
Slide39My reputation is on the line!
Even if a “evil” peer convinces a popular intermediary to recommend him, his own good standing will reduce and he will no longer be popular.
One hop
reputation
Reputation
Slide40Peers can gain good standings by:
1) Contributing to an
intermediary directly
2)
Contributing to a peer that satisfied the intermediary request in the pastIn (1), there is an increase of reputation in the intermediary. In (2), reputation of one peer is simply moved to the other.One hop reputationLiquidity
Slide41Total reputation (liquidity of intermediary) is limited by its
own demand
.
Solved by
inflation factor of
100.Inflation also provide incentive to be intermediary.Potential for economic inflation.One hop reputationLiquidity
Slide42One hop reputation
Evaluation
Slide43Data overhead
Maximum overhead for popular
intermediary
of 3MB per day.
How good is the one hop coverage (
In BT-2 trace)?Median number of shared intermediaries is 83More than 99% of peers have at least one common entry in their top K.One hop reputationEvaluation
Slide44How quickly can a new peer determine intermediary value?
After a few dozen interactions with randomly chosen peers.
One hop
reputation
Evaluation -
New Users
Slide45How quickly can a new peer gain reputation?
Peers observe popular intermediaries frequently.
New users both encounter opportunity to gain standings and recognize it.
What can they exchange?
One hop
reputationEvaluation - New Users
Slide46Evaluation show concrete improved performance, regardless of strengthened incentives.
25 MB file download was tested on
PlanetLab
Historical information allows peers to find good tit-for-tat peering.
Results show a
median reduction in download time from 972 seconds to 766 secondsOne hop reputationEvaluation – Performance
Slide47Questions?
Slide48[1]
Efficientcy
and Nash
equilibria
in a scrip system
for P2P networksIan A. Kash, Eric J. Friedman, Joseph Y. Halpern[2] Optimizing scrip systems: Eciency, crashes, hoarders, and altruistsIan A. Kash, Eric J. Friedman, Joseph Y. Halpern[3] One hop Reputations for Peer to Peer File Sharing WorkloadsMichael Piatek Tomas Isdal Arvind Krishnamurthy Thomas Anderson. Papers