University of WürzburgInformatik III (Distributed Systems)Prof. Dr. P. Tran-Gia

www3.informatik.uni-wuerzburg.de

Towards Efficient Simulation of Large Scale P2P

NetworksTobias Hoßfeld

ITG-Fachgruppe 5.2.1“Cooperating and Scalable Networks”

Aachen, Ericsson, J. Sachs, 04.05.2006

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University of Würzburg

Distributed Systems

T. Hoßfeld

Cartography of P2P Architectures

Two control functions in P2P systems

Resource mediationwhere are files located

Resource access controlwho may download a file and when

Mapping of P2P architectures into architectural space pure P2P hybrid P2P classic client/server

Identification of control objectives

Client/Server

PureP2P

X eDonkey

X BitTorrent

X Gnutella

X Chord

resource access controlcentralized decentralized

res

ou

rce

me

dia

tio

nce

ntra

lized

dece

ntra

lized

P2P Cartography

operator-centricdomain

user-oriented domain

hybrid P2P

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University of Würzburg

Distributed Systems

T. Hoßfeld

Structured P2P-Network (Chord)

InformationProvider

InformationMediator

InformationSeeker

Peer-to-Peer Architectures

Unstructured P2P-Network (Gnutella)

InformationProvider

InformationSeeker

BitTorrent

TrackerWeb-Server

InformationMediator

Hybrid P2P-Network (eDonkey)

Index

IndexInformationMediator

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University of Würzburg

Distributed Systems

T. Hoßfeld

Basic Functions of P2P Networks

join() leave()new nodeknows

one peer

bootstrapserver

contacts

position in the P2P network according to the structure: self-organization

node failures detected by periodical updates or not answered requests: resilience

peer informs its “neighbors” and bootstrap server

bootstrapserver

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University of Würzburg

Distributed Systems

T. Hoßfeld

Basic Functions of P2P Networks (contd.)

insert(key,data) retrieve(key)

peer x wants to insert (key,data); using DHTs: key = hash(data)

x

key routes to peer y which stores datay

structure assigns responsibility for data based on hash function: load balancing

y responsible for (key,data)

peer x searches for key

y has (key,data)

xand asks its neighborswhich redirect requests

y sends data to x

performance improved by using shortcuts according to the known structure for a given key:scalability

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Distributed Systems

T. Hoßfeld

Main feature is multiple source download. Peers issue several download requests for the same file to

multiple providing peers in parallel. Providing peers serve the requesting peers simultaneously.

Basic Function of P2P Content Distribution

index server #1

index server #2

downloading peer 1

providingpeer

providingpeer

providingpeer

downloading peer 2downloading

and providingpeer 1

After successfully downloading a whole

chunk, it is provided to other peers.

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University of Würzburg

Distributed Systems

T. Hoßfeld

Features of P2P Systems and Their Implications

Usually a large number of participating peers Large-scale: a lot of nodes and even higher number of resources

need to be simulated

Peers may arbitrary join or leave Highly dynamic: a lot of user created event (due to churn, i.e.

peers joining and leaving arbitrarily, as key feature of P2P systems)

Cooperative working of peers and robust systems Complexity:

one event can cause a large number of events at other peers, i.e. system events, due to cooperation among peers

additionally periodic or provisional systems event to cope with the self-organization of p2p systems

Target of Workshop:Focus on large-scale P2P networks in order to consider key characteristics (e.g. regarding churn for 100 peers reasonable?)

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University of Würzburg

Distributed Systems

T. Hoßfeld

Approach For Simulating Large-Scale P2P Systems

System state has to be stored at simulation machine requires efficient data structures (e.g. calendar queue)

How to model in order to reduce the number of events? Resource mediation might not require to model bandwidth,

only signalling delay Resource exchange might not require to model delay if large

contents are exchanged; requires modelling of bandwidth other performance influence factors: packet loss, moving

users, … appropriate abstractions & models for investigated application

Clustering of peers to user groups... might allow parallel simulation of clusters

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University of Würzburg

Distributed Systems

T. Hoßfeld

Workshop in Würzburg

Efficient Data Structures Andreas Binzenhöfer, Calendar Queue and Event Design

Algorithms Jens Oberender, Modelling Resource Fragmentation

Abstractions and Models Kolja Eger, Packet-based Simulation Gerald Kunzmann, Signaling in Voice/Video over IP Systems Daniel Schlosser, Tobias Hoßfeld, Periodic and Market-Based

Bandwidth Allocation

Parallel Simulation Ivan Dedinski, Parallel Discrete Event Simulation

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University of Würzburg

Distributed Systems

T. Hoßfeld

Talks Today

abstract

detailed

abstract

detailedunoptimized

adapted

Modeling Resource Access Control

Modeling Resource Mediation

Co

mp

ac

t

D

ata

Str

uc

ture

s

TobiasHoßfeld

KoljaEger

GeraldKunzmann

AndreasBinzenhöfer Hier könnte Ihr

Name stehen !

University of WürzburgInformatik III (Distributed Systems)Prof. Dr. P. Tran-Gia

www3.informatik.uni-wuerzburg.de

Periodic and Market-Based Bandwidth Allocation in

eDonkey Networks

Tobias Hoßfeld, Daniel Schlosser

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University of Würzburg

Distributed Systems

T. Hoßfeld

Measurements of eDonkey Traffic

Case-by-case measurements of eDonkey file-sharing application in public GPRS/UMTS network

Multiple source download via GPRS

0 5 10 15 20 25 300

5

10

15

20

25

30

35

40

45

time [min]

thro

ug

hp

ut

[kb

ps

]

downloading peer (mobile)sharing peer #1 (mobile)sharing peer #2 (fixed)

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University of Würzburg

Distributed Systems

T. Hoßfeld

eDonkey Data Exchange via UMTS

UMTS upload restricts throughput

UMTS download restricts throughput

0 2 4 6 8 100

1

2

3

4

5

time [min]

da

ta [

MB

]

fixed downloading peerfixed sharing peer #1mobile sharing peer #2

0 5 10 15 20 25 300

1

2

3

4

5

time [min]

da

ta [

MB

]

mobile downloading peerfixed sharing peer #1mobile sharing peer #2

Max-min-fair share of available bandwidth is observed How to model the bandwidth allocation of fair-share P2P file-sharing

applications?

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University of Würzburg

Distributed Systems

T. Hoßfeld

Simulation of Fair-Share Bandwidth Allocation

Events which influence the bandwidth allocation are that a peer… starts the download of a file finishs a download goes offline while downloading continues downloading a file after joining the network again

We consider eDonkey-like file-sharing networks

Aim: Modeling of bandwidth allocation in fair-share networks

tEvents

tComputation ofallocated bandwidth

t t t t t t t t t t t

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University of Würzburg

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T. Hoßfeld

Stream-based or packet-based approach?

TCP can be neglected if conditions are fulfilled (540 kB blocks) Signaling vs. data exchange: RTT vs. bandwidth

0 50 100 150 200 250 300 350 4000

50

100

150

200

250

300

350

400

downlink bandwidth [kbps]

go

od

pu

t [k

bp

s]

RTT = 100msRTT = 200msRTT = 300ms

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University of Würzburg

Distributed Systems

T. Hoßfeld

What means fair-share?

Peer 311 kbps

Peer 23 kbps

Peer 540 kbps

Peer 420 kbps

uploadingPeer 140 kbps

3 kbps11 kbps

13 kbps 13 kbps

All peers get the same bandwidth If a peer cannot consume completely the allocated

bandwidth, the surplus is distributed among the remaining peers

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University of Würzburg

Distributed Systems

T. Hoßfeld

Periodic Bandwidth Allocation

Peer 1

Peer 2

Peer 5

Peer 3

Peer 4

For each t, for each peer: compute bandwidth allocation

Allocated bandwidth can be

overbooked or underbooked

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University of Würzburg

Distributed Systems

T. Hoßfeld

Market-Based Bandwidth Allocation

For each event, consider affected, i.e. connected, peers

All affected peers make a bid

Strategy If there are no other bids, propose

x = not allocated bandwidth / #peers If minimal bid y of all affected peers is smaller than x, then

keep bid y and compute x‘ If all bids are larger than x, then bid x on these

connections

Finish: If lower bid of a connection is the minimal bid of a peer and is repeated

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University of Würzburg

Distributed Systems

T. Hoßfeld

Market-Based Bandwidth Allocation

Uploadingnetwork links

Downloadingnetwork links

NL8: 40kbps

NL1: 10kbps

NL2: 80kbps

NL3: 40kbps

NL4: 30kbps

NL5: 10kbps

NL6: 10kbps

NL7: 10kbps

NL0: 10kbps3.333

5

20

15

10

20

5

10

40Initial bid: x = BW / #peers

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University of Würzburg

Distributed Systems

T. Hoßfeld

25

MBBA Example

Uploadingnetwork links

Downloadingnetwork links

NL8: 40kbps

NL1: 10kbps

NL2: 80kbps

NL3: 40kbps

NL4: 30kbps

NL5: 10kbps

NL6: 10kbps

NL7: 10kbps

NL0: 10kbps3.333

5 10

20

15

20

5

10

40

3.333

26.667

5 3.3336.667

25

25

If minimal bid y of all connected peers holds y<x, then set bid y and compute x‘ x = BW / #peers.

If all y<x, keep x.

21

University of Würzburg

Distributed Systems

T. Hoßfeld

3.333

MBBA Example

25

Uploadingnetwork links

Downloadingnetwork links

NL8: 40kbps

NL1: 10kbps

NL2: 80kbps

NL3: 40kbps

NL4: 30kbps

NL5: 10kbps

NL6: 10kbps

NL7: 10kbps

NL0: 10kbps3.333

5 10

20

10

40

26.667

5 3.3336.667

25

25

Finish: If lower bid of a connection is the minimal of a peer and is repeated

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University of Würzburg

Distributed Systems

T. Hoßfeld

MBBA Example

5

25

2525

Uploadingnetwork links

Downloadingnetwork links

NL8: 40kbps

NL1: 10kbps

NL2: 80kbps

NL3: 40kbps

NL4: 30kbps

NL5: 10kbps

NL6: 10kbps

NL7: 10kbps

NL0: 10kbps

3.333

10

40

26.667

6.667

5

6.66724.444

24.44424.444

Finish: If lower bid of a connection is the minimal of a peer and is repeated

Minimal bid y<x, set y and compute x‘

23

University of Würzburg

Distributed Systems

T. Hoßfeld

24.44424.444

24.444

MBBA Example

Uploadingnetwork links

Downloadingnetwork links

NL8: 40kbps

NL1: 10kbps

NL2: 80kbps

NL3: 40kbps

NL4: 30kbps

NL5: 10kbps

NL6: 10kbps

NL7: 10kbps

NL0: 10kbps

3.333

10

40

26.6675

6.667

10

31.667

31.667

Finish: If lower bid of a connection is the minimal of a peer and is repeated

If all y<x, keep x.

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University of Würzburg

Distributed Systems

T. Hoßfeld

31.667

MBBA Example

Uploadingnetwork links

Downloadingnetwork links

NL8: 40kbps

NL1: 10kbps

NL2: 80kbps

NL3: 40kbps

NL4: 30kbps

NL5: 10kbps

NL6: 10kbps

NL7: 10kbps

NL0: 10kbps

3.333

10

40

26.6675

6.667

36.666

Finish: If lower bid of a connection is the minimal of a peer and is repeated

25

University of Würzburg

Distributed Systems

T. Hoßfeld

MBBA Example

Uploadingnetwork links

Downloadingnetwork links

NL1: 10kbps

NL2: 80kbps

NL0: 10kbps

3.333

10

26.6675

6.667

36.666

NL8: 40kbps

NL3: 40kbps

NL4: 30kbps

NL5: 10kbps

NL6: 10kbps

NL7: 10kbps

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University of Würzburg

Distributed Systems

T. Hoßfeld

Comparison PBA vs. MBBA

Exchange of small files

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University of Würzburg

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T. Hoßfeld

Comparison PBA vs. MBBA

Exchange of large files

MBBA: computes and allocates immediately fair-share bandwidth -> in real systems this requires some time

PBA: bandwidth overbooked or underbooked for Δt -> in next step bandwidth is adapted

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University of Würzburg

Distributed Systems

T. Hoßfeld

Conclusion

For P2P content distribution networks, like eDonkey, resource access control is crucial point

Fair-share bandwidth allocation has to be modeled

We have proposed two stream-based approaches which are valid in the considered scenarios Periodic bandwidth allocation PBA Market-based bandwidth allocation MBBA

Depending on the number of events influencing the resource access control PBA or MBBA has to be preferred