outline cas project presentation aim of the project

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CAS Project Presentation

Andrea Marcozzi

27/03/2008

Dipartimento di Scienze

dell’Informazione, Università di

Bologna

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Outline

! Aim of the project

! ResourceWorld

! Project Specifications

! Peersim guidelines

! Clustering Coefficient and Average Path

Length

! Churn

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Aim of the project

! The aim of this project is to take an

existing protocol for P2P networks

called ResourceWorld

! To propose 2 modifications of such

protocol

! Analyze the results obtained with this

two modifications in order to understand

the global emergent mechanism

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ResourceWorld

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ResourceWorld Introduction

! In several peer-to-peer applications, systemsrequire their member nodes to behave in asocially beneficial way

! This approach is in contrast with the principle ofclassical individual rationality that states agentsshould select actions that maximize theirindividual utility

! ResourceWorld is a simple locally adaptiveprotocol that claims to produce socially rationaloutcomes through a process of self-organisationeven though nodes only act on their own utilityvalues

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The ResourceWorld scenario

! We consider a situation in which there arepopulations of peer nodes that can storeand serve some number of resources (i.e.:Files)

! Each node has its own single nativeresource that it wishes to serve to someusers community achieving somespecified service level

! After each interaction, nodes get anappropriate utility

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The ResourceWorld Model (I)

! Let’s consider a P2P overlay network in whicheach node has the following state:

– Strategy bit, A ! {0,1} (indicates if the nodeis altruistic or not)

– Skill, S ! {1,2,3,4,5} (indicates the resourceof the node)

– Neighbour list (max 20 neighbours)

! Periodically, with probability 0.5, nodes receivea request to be completed (Job, J !{1,2,3,4,5})

! Periodically, with a small probability, tworandom nodes are selected and their utilitiesare compared (SLAC) 7

System architecture

0. Newscast

(Peer Sampling Service)

1. SLAC

2. RW

(Application)

utility

Random node

(movement)

Cooperative

neighbor

Three protocols: Newscast, SLAC,

ResourceWorld (RW)

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The ResourceWorld Model (II)

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ResourceWorld Pseudo-code

At each cycle with probability 0.5:

each node i receives a certain job

if ( Si == job) then Ui += 1;

else

if ( STRATi == true ) then

for all neighbors j with random order

if ( Sj == job) then

Ui += -0.25;

Uj += 1;

With probability 0.2 the SLAC algorithm takes place

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Experiments (parameters)! Peersim (http://peersim.sf.net)

! SLAC

! Network size: 4000 nodes

! Network degree: 20

! Initial topology: random

! Single node initialization:

– Strategy bit: 0 (defect)

– Skill: random

! Mutation: (MRT = 0.01; MRS = 0.005)

! Payoffs:

– Benefits from 0.1 to 2.0 by 0.1 steps

– Costs from 0.1 to 2.0 by 0.1 steps

! 10 runs for each experiments meaning

20 X 20 X 10 runs 11

Experiments (results) (I)

The utility measure we used to evaluate the

performances of the model is the Pcj : the

percentage of completed jobs

We found that to obtain a Pcj > 80%, the

Benefit payoff must be greater or equal

than the Cost payoff

When Cost > Benefit, we obtain a very low Pcj

When Cost = Benefit, the system takes longer

to reach Pcj > 80%

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Experiments (results) (II)

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ResourceWorld Conclusions

! We took the ResourceWorld model and

modified both benefit and cost utilities to

explore a large space of possible values

! In each case we checked if the protocol

maximized the collective utility or not

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Project Specification

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Implementation part

! Define 2 new mechanism of “Jobpassing”

– Pass only to altruistic nodes

– Job executed by altruistic nodes only

– Ordered visit of neighbors list

– Recursive job passing (small degree)

– …

! The global emergent mechanism mustbe understood

– Results must be compared and explainedwhatever they are

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Beware not to cheat!

! Everything is based on local knowledge

! Other nodes’ state known only when

interacting with them

! It’s forbidden to chose nodes directly

from the network structure

– No global knowledge

– Only local views available for each node

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Elaboration of the Data

! Examine the effects of the new proposed

mechanisms (through Observers):

– Cooperation

• Cooperation level (Percentage of coop nodes)

• Percentage of completed jobs (Pcj)

• Time for cooperation and Pcj

– Cooperation under Churn

• Same as above, but with churn

– Topology

• Clustering coefficient (CC)

• Average path length (APL)

• Network Snapshots

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Elaboration of the Data (II)

! Examine initial settings effects on

performances

– Network size

– Network degree

– Skills number

– SLACER dropping parameter (W)

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Provided code

! The package casProject provides:– Slacer

• Slacer protocol

• SLACERapp must be extended

• BooleanStrategyObserver (cooperating nodes)

• SlacerGraphPrinter (observer for snapshots)

– casProjectApp• casProject is the code you must write (extends

SLACERapp)

• casProjectObserver (Percentage of completed jobs)

! Additional Classes– If you need some additional mechanism feel free to

implement and use it!• New specific observer

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Peersim Guidelines

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Implementing new classes

! All the new code should be included in

classes implementing one (or more) of

the main peersim interfaces

– CDProtocol

– Linkable

– Control

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Implementing CDProtocol interface

! peersim.cdsim.CDProtocol provides asingle method to describe protocolactions

– Void nextCycle (Node node, intprotocolID);

•node is the current running node

•protocolID is the protocol id in the currentnode protocol stack

– In nextCycle() any action performed by eachnode at each simulation cycle should beimplemented

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Implementing Linkable interface

! peersim.core.Linkable provides abstract

methods for node view management

– public int degree();

– public Node getNeighbor(int i);

– public boolean addNeighbor(Node neighbor);

– public boolean contains(Node neighbor);

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Implementing Control interface

! peersim.core.Control provides a single

method to describe global network

actions

– boolean execute();

! In execute(); any action performed at

each simulation cycle for global

management

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Defining configuration parameters

! It is possible that components need

parameters to be passed by

configuration file

! peersim.config.Configuration provides

utilities to read parameters from

configuration file

– Numeric values

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CDProtocol implementation example

public class casProject extends SLACERapp{

// configuration parameter definition

public static final String PAR_SKILLS = "numskills";

public casProject(String prefix) {

numSkills=Configuration.getInt(prefix+"."+PAR_SKILLS,5);

}

public void nextCycle( Node node, int protocolID ){

// protocol behavior

}

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Control implementation examplepublic class casProjectObserver implements Control

{

public static final String PAR_PROT = "protocol";

public casProjectObserver(String name){

this.name = name;

pid = Configuration.getInt(name + "." + PAR_PROT);

}

public boolean execute(){

IncrementalStats queries=new IncrementalStats();

IncrementalStats completed=new IncrementalStats();

for (int i = 0; i<Network.size(); i++){

casProject protocol = (casProject) Network.get(i).getProtocol(pid);

queries.add(protocol.queries);

completed.add(protocol.completed);

}

double ratioComp=((double)completed.getSum()) / queries.getSum();

System.out.println(ratioComp);

Return false;

}

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Configuration File (I)#random.seed 1150540268549

simulation.cycles 300

network.Shuffle

network.size 4000

protocol.0 example.newscast.SimpleNewscast

protocol.0.cache 20

protocol.1 slacer.Slacer

protocol.1.degree 20

protocol.1.linkable 0

protocol.1.application 2

protocol.1.reprprob 0.20

protocol.1.tagmutation 0.01

protocol.2 slacer.casProjectApp.casProject

protocol.2.linkable 1

protocol.2.numskills 5

protocol.2.queryprob 0.5

protocol.2.completed 1

protocol.2.processed -0.2

General settings

Protocols settings

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Configuration File (II)

init.0 peersim.dynamics.WireKOut

init.0.protocol 0

init.0.k 20

init.1 slacer.SlacerInitializer

init.1.protocol 1

init.1.isolateprob 1

init.1.copyprob 1

init.2 slacer.casProjectApp.casProjectStrategyInitializer

init.2.protocol 2

init.2.quantity 0

Init.2.numskills 5

Init.2.skill_m 0.001

Init.2.strat_m 0.001

init.3 peersim.dynamics.WireKOut

init.3.protocol 1

init.3.k 20

Init.3.undirected

Control settings:

initialization

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Configuration File (II)

control.ob0 slacer.BooleanStrategyObserver

control.ob0.protocol 2

control.ob1 slacer.casProjectApp.casProjectObserver


control.ob2 slacer.SlacerGraphPrinter


control.ob2.outf RWplot/

control.ob2.format dot

control.ob2.period 20

control.ob4 peersim.reports.GraphStats


control.ob4.period 50

control.ob4.nl 100

control.ob4.nc 100

Control settings:

Observers

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Working with Peersim

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Getting Peersim

! Peersim is available at

http://peersim.sourceforge.net

– (I suggest using peersim 1.0.1)

! Java 5 must be used, SunJava

JDK5.0(J2SE 1.5.0)

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Compiling and Launching

! The easiest way for compiling peersim is

through this command:

javac -cp .:jep-2.3.0.jar:djep-1.0.0.jar `find . -name "*.java"`

To run a simulation the command you should

use is:

java -cp .:peersim-1.0.1.jar:jep-2.3.0.jar:djep-1.0.0.jar

peersim.Simulator slacer/casProjectApp/RW-conf1

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Plotting the results

! Log files can easily be plotted using

Gnuplot or dragging them into Excel

! For the Network Snapshots, an easy

linux utility called Neato can be used

! For further information on Gnuplot and

Neato go to:– http://www.gnuplot.info/documentation.html

– http://www.graphviz.org/Documentation/neatoguide.pdf

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Clustering Coefficient and

Average Path Length

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Clustering Coefficient (CC)

! The CC for node i is the proportion of:

– links between the vertices within itsneighbourhood

– the number of links that could possiblyexist in i neighborhood

! If the graph is directed the number of

possible links is:

! It the graph is undirected the number

of possible links is:

! Network CC:

– Is the average of every node’s CC

!

ki(k

i"1)

!

ki(k

i"1)

2

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Average Path Length (APL)

! Path length between node i and node j

– Minimum number of hops needed to gofrom i to j

– If j is a neighbor of i, the path length is 1

! APL is defined as the average number

of steps along the shortest paths for all

possible pairs of network nodes

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Small World Networks

! A network with high CC and low APL is saidto be a Small World Network

! A Small World Network is a class of randomgraphs where most nodes are not neighborsof one another, but most nodes can bereached from every other by a small numberof hops or steps– High CC

– Small APL

! Examples– People acquaintance (social networks)

– Internet connectivity

– Gene networks 39

Calculating CC and APL with

Peersim

! Use the component:

peersim.reports.GraphStats

! Parameters:

– Protocol: the id of the protocol on which calculate

the CC and APL

– Period: every how many cycles

– nl: number indicating the dimension of the sample

of nodes on which to calculate the APL

– nc: number indicating the dimension of the sample

of nodes on which to calculate the CC

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Churn

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Churn can have several effects on a P2P system:

• data objects may be become unavailable if all replicas disconnect

• routing tables may become inconsistent

(e.g., entries may point to disconnected nodes)

• the overlay may become partitioned if many nodes suddenly

disconnect:

Effects of churn

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Churn in peersim

! Use the component:

peersim.dynamics.DynamicNetwork

! Parameters:

– Add: number of nodes to add/remove

– Substitute

– Step

– From: starting cycle

– Until: end cycle

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If you need any help

! Peersim resources page:

http://peersim.sf.net

! These slides on:

http://www.cs.unibo.it/~marcozzi

! For any questions:

[email protected]

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References

! Hales, D. and Arteconi, S. (2006) Slacer: A self-organizing protocol for coordination in p2pnetworks. IEEE Intelligent Systems

! Hales, D. (2004) From selfish nodes tocooperative networks - emergent link basedincentives in peer-to-peer networks. In Proc. ofthe 4th IEEE International Conference on Peer-to-Peer Computing (P2P2004). IEEE ComputerSoc. Press

! Marcozzi, A. and Hales, D. (2008) EmergentSocial Rationality in a Peer-to-Peer System.Special Issue of ACS. (In press)

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