Advanced Network Architecture Research Group Overview · 1 Oct 05, 2005 APCC2005 11 Advanced Network Architecture Research Group Osaka University A Flooding Method for Exchanging

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Oct 05, 2005Oct 05, 2005 APCC2005APCC2005 11

Advanced Network Architecture Research GroupAdvanced Network Architecture Research Grouphttp://http://www.anarg.jpwww.anarg.jp//

Osaka UniversityOsaka University

A Flooding MethodA Flooding Methodfor Exchanging Routing Informationfor Exchanging Routing Information

in Powerin Power--Law NetworksLaw Networks

Nobutaka [email protected]

Osaka University, Japan

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Overview

BackgroundObjectiveFlooding in power-law networks

Examine the problems of flooding on the Internet-like topology

Proposed methodMechanism and Evaluation

Conclusion and future works

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Background(1/2)

Flooding and its problem

Flooding is used to deliver the routing information to entire network

Each node copies a message to all the neighboring nodesSome nodes receive the same duplicated messages via different routes

If the size of a network increases Amount of traffic becomes critical problem [2] by increasing messages Example of flooding and

how the duplication occurs

[2] L. Gao and J. Rexford, “Stable Internet routing without global coordination,” in Proceedings ofACM SIGMETERICS 2000, pp. 307-317, June 2000.

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Background(2/2)

Topological characteristic of the Internet

Internet topology follows “Power-law”

Connectivity of nodes exhibits power-law attributes

How the flooding in power-law networks behaves?

Messages tend to concentrate on nodes with many connectionsIt is important to know the feature of Internet-like networks

Many nodes have a few connections to other nodes

A few nodes have many connections to other nodes

Distribution of the number of neighboring nodes in 1000 nodes power-law network.

P(K

) : C

ompl

emen

tary

Cum

ulat

ive

Dis

tribu

tion

Func

tion

0.001

0.01

0.1

1

1 10 100K : number of links

Nodes with 2-4 links occupies 80%

Four nodes with 50-80 links

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Clarify a behavior of simple flooding in Power-Law networks

Estimate the amount of control messagesExamine problems with a simple flooding method

Propose a new flooding methodBased on the observation found aboveDecreases the number of control messages

Objective

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Evaluation of simple flooding (1/2)

Duplicate messages in Power-Law network

Simulation setupTopology generated by BA model [4] with 1000 nodes (m=m0=2)Failure on a node with many connectionA failure occurred at the highest degree node

Messages concentrate on nodes with many links

[4] A. Barabasi and R. Albert, “Emergence of scaling in random networks,” Science, vol. 286, pp. 509–512, Oct. 1999.

Number of control messagesgenerated by single-node failure

on 1000-node power-law network.

012345678910time (ms)

0200

400600

8001000

0500

100015002000250030003500

cum

ulat

ive

num

ber o

f dup

licat

ed p

acke

ts

012345678910time (ms)

0200

400600

8001000

0500

100015002000250030003500

cum

ulat

ive

num

ber o

f dup

licat

ed p

acke

ts

Nodes with many links

Cum

ulat

ive

num

ber o

f du

plic

ate

mes

sage

s

Messages concentrate on nodes with many connections

time (ms) Nodes with a few links

Flooding in power-law network does not scale well

2

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Evaluation of simple flooding (2/2)

Duplicate messages in different networksCompared with random networks

Connections between nodes are setup randomly

Many duplication found in power-law network

Duplication increased rapidly in power-law networksDuplication increased slowly in random networks

A method to reduce messages is required to perform flooding in large power-law networks

Maximum number of duplicated messages dependent on network

size (averaged over 10 experiments)

num

ber o

f dup

licat

e m

essa

ges

(x10

0)

number of nodes

0.1

1

10

100

1000

10000

1000 10000

power-law network

random network

Increased quickly in power-law networks

Increased slowly in random networks

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Osaka UniversityOsaka UniversityProposed method (1/3)

Overview of proposed method

(i) Probabilistic floodingEach node relays messages to a part of nodes chosen by a relaying probability pDuplicated messages are reduced at intermediate nodes

Some nodes do not receive messages by its nature But in routing protocols, all nodes have to receive routing information

A node originates flooding

Flooding continues with probability p

！！

Our method consists from two ideas

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Overview of proposed method(ii) Periodic information

exchange Each node asks its neighborsweather they have a new informationIf new information is found, a node receives it from the neighbor node

Nodes that couldn’t received control messages can get routing information Using this mechanism, we can choose lower relaying probability

A node originates flooding

Flooding continues with probability p

Deliver information to all nodes by periodic exchange

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Choosing relaying probability pHow many nodes receive information by periodic exchange?

About 15% of nodes according to the simulation result(not presented here)

Relationship between probability p and the number of nodes received messages

Probability p=0.1 ~ 0.9 (fig.)p=0.6 enables this method to inform 85% of nodes

time (ms)0

5001000150020002500300035004000

0 2 4 6 8 10 12 14 16

Simple flooding

p=0.9p=0.8p=0.7p=0.6p=0.5

p=0.4p=0.3

p=0.2p=0.10

5001000150020002500

0 2 4 6 8 10 12 14 16

p=0.4p=0.3

p=0.2p=0.100 2 4 6 8 10 12 14 16

p=0.4p=0.3

p=0.2p=0.100 2 4 6 8 10 12 14 16

p=0.4p=0.3

p=0.2p=0.1

Num

ber o

f con

trol

mes

sage

s (c

umul

ativ

e)N

umbe

r of n

odes

rece

ived

in

form

atio

n (c

umul

ativ

e) p=0.6p=0.5p=0.4p=0.3

p=0.2 p=0.10

102030405060708090

100

0 2 4 6 8 10 12 14 16

p=0.9p=0.8p=0.7

p=0.3

0102030405060708090

100

0 2 4 6 8 10 12 14 16

Simpleflooding

time (ms)

Delivers to about 85% of nodes

with p=0.6

Decreases about 40% of messages

with p=0.6

The evaluation result of probabilistic flooding with p from 0.1 to 0.9

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Osaka UniversityOsaka UniversityEvaluation of proposed method (1/3)

Simulation experimentsTwo scenarios of flooding

Flooding from a single nodeFlooding from different nodes

Evaluate two indexesThe number of nodes that received messagesAmount of control messages

Compared with two methodsSimple floodingFlooding based on critical probability (p =0.9)

With this probability, 99.9% of nodes receive messages [6]

Other parametersPeriodic exchange is performed at every 5 secondsEvaluated on 1000 nodes power-law network (BA model : m0=m=2)

[6] F. Banaei-Kashani and C. Shahabi, “Criticality–based analysis and design of unstructured peer-to-peer networks as ’complex systems’,” in Proceedings of GP2PC, pp. 22–32, May 2003.

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Flooding from a single nodeSimple flooding and probabilistic flooding

Spread messages in short time, but cannot decrease messages

Proposed methodDelivers to 85% of nodes in short timeDecreases about 50% of messages

Periodic exchange delivers information to all nodes

Evaluation result of single flooding in 1000-node power-law network

Num

ber o

f nod

es

rece

ived

info

rmat

ion

(cum

ulat

ive)

Num

ber o

f con

trol

mes

sage

s (c

umul

ativ

e)

Time (sec)

Full Flooding

SimpleProbabilityMethod

FSLS

Proposed Method

0

1000

2000

3000

4000

5000

6000

7000

8000

0 1 2 3 4 5 6

Simple Flooding

Probabilistic flooding

FSLS

Proposed Method

0

200

400

600

800

1000

0 1 2 3 4 5 6Probabilistic flooding Probabilistic flooding

decreases 10% of decreases 10% of messagesmessages

Proposed method Proposed method decreases 50% of decreases 50% of

messagesmessages

Probabilistic flooding Probabilistic flooding spreads information in spreads information in short timeshort time

Periodical exchange Periodical exchange makes the nodes which makes the nodes which didndidn’’t receive messages t receive messages

to be informedto be informed

Proposed method Proposed method delivers information delivers information

to 85% of nodes to 85% of nodes quicklyquickly

3

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Flooding from different nodes

Flooding frequencyFlooding performed based on Poisson arrival with a mean 1 / sec

Proposed methodReduced 50% of messages compared to simple flooding

Num

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e)N

umbe

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odes

rece

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in

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umul

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Time (sec)0

100000200000300000400000500000600000700000

800000

0 5 10 15 20 25 30 35 40 45 50

Simple flooding

SimpleProbabilityMethod

FSLS

Proposed Method

0

10000

20000

30000

40000

50000

60000

70000

0 5 10 15 20 25 30 35 40 45 50

FSLS

Proposed Method

0

10000

20000

30000

40000

50000

60000

70000

0 5 10 15 20 25 30 35 40 45 50

Simple flooding

Probabilistic flooding

Evaluation result of flooding from different nodes in 1000-node power-law network

Reduced 50% Reduced 50% of messages of messages compared to compared to

simple floodingsimple flooding

Reduced 40% Reduced 40% of messages of messages compared to compared to probabilistic probabilistic

floodingflooding

Our method scales better than simple flooding

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Conclusion and Future worksA flooding in power-law network

Didn’t scale well due to the concentration of messages at a part of nodes

Proposed flooding methodProbabilistic flooding with periodic exchange of informationDecreased 50 % of control messages compared to simple flooding method

Future worksTheoretical deriving of relaying probabilityEvaluate in other network models that generate power-law attribute

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Documents

Advanced Network Architecture Research Group Overview · 1 Oct 05, 2005 APCC2005 11 Advanced Network Architecture Research Group Osaka University A Flooding Method for Exchanging