1 © nokia presentation_name.ppt / 13-11-2003 / jukka k. nurminen optimaalisen tietoliikenneverkon...

1 © NOKIA Presentation_Name.PPT / 13-11-2003 / Jukka K. Nurminen

Optimaalisen tietoliikenneverkon suunnittelu

Jukka K. Nurminen

Nokia Research Center

[email protected]


Cellular Network Planning

1. Radio network

2. Transmission network

3. Packet network


Radio Network Planning

• Composite Coverage displays

• Signal propagation models• Heuristic frequency allocation algorithms


Plan the transmission network to connect GSM basestations with

• minimum cost• high reliability• easy maintenance• flexibility for growth• …

Transmission Network Planning Tool

• Shortest path routing• Two disjoint paths routing• Multilayer routing• Topology generation

•Min spanning tree•Traveling salesman


Packet Network Modeling

• IP, ATM

• Parameters (random variables):• packet inter-arrival time• number of packets in a packet call• packet length• reading time between packet calls

D O C U M E N T T Y P E

T y p e U n itO r D e p a r tm e n tH e r eT y p e Y o u r N a m e H e r e T y p e D a te H e r e

P a c k e ta r r iv a ls

P a c k e t c a ll

R e a d in g t im eb e tw e e n p a c k e t

c a lls

t im e


Interrupted Poisson Process (IPP)

ON period -exponentially distributed with mean 1/ON

OFF period -exponentially distributed with mean 1/OFF

Arrival rate -exponentially distributed with parameter a

Packet length

OFF

ON

a

OFF ON

ON period

OFF period


Queuing Models

Queuing models are suitable for describing (modeling) packet switched nodes and networks

buffer server

Arriving

packets

Departing

packets

Outgoinglink

L

C

• - packet arrival rate• L – average packet length• C – link capacity•1/ – average service time• W – waiting time in the buffer• d – delay• N – number of packets in the system• - traffic load

The mean delay in the node is equal to the sum of the mean waiting time in the buffer and the mean service time

E[d]=E[W]+1/ = E[W]+L/C


Peer-to-peer Network Analysis

1. Content sharing

2. Distributed computing


Content sharing

• Napster

• Systems based on FastTrack

KaZaA

Grokster

Morpheus (earlier)

• Gnutella

LimeWire

Shareaza

Morpheus (newer)

Centralized

Distributed

MP3 music, pictures, DVD movies, etc.


Search A.mp3

1. Register

2. Search

3. Transfer

Napster

How it works?Gnutella

1. Search

5. Transfer2. Propagate search

0. Connect

4. Hit found


Topologies for Content Sharing

• Reported: power law (“fit get fitter”, “rich get richer”) evolved into multimodal very dynamic

cannot be easily emulated

• Chosen options:• Random: nodes have random number of connections• Semi: nodes have random number of connections, at least 1• ConMesh: nodes connect to existing network• ConStars: ConMesh + leaf nodes

Random mesh Connected mesh Connected starSemi-random meshRandom mesh Connected mesh Connected starSemi-random mesh


Simulation results

0%

20%

40%

60%

80%

100%

2 2.2 2.4 2.6 2.8 3 3.2 3.4 3.6 3.8 4

Node degree

Random Semi

ConMesh ConStars 0%

90%

91%

92%

93%

94%

95%

96%

97%

98%

99%

100%

2 2.2 2.4 2.6 2.8 3 3.2 3.4 3.6 3.8 4

Node degree

ConStars 2% ConStars 4% ConStars 10%

ConStars 20% ConStars 40%


Distributed computing

• Currently on distributed PCs• Can such applications run on mobile phones?


Client 3Client 2Client/Server 1

Client 4

Client 5

Root server

Lucas-Lehmer test:2p-1 is prime if and only if S(p-1) = 0 (mod 2p-1),where S(1) = 4 and S(k+1) = S(k)2-2.

Personal Java on Nokia Communicator

mobilephone+

1 © nokia presentation_name.ppt / 13-11-2003 / jukka k. nurminen optimaalisen tietoliikenneverkon...

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