OPTICAL PACKET SWITCHING TECHNIQUES

BY

WALTER PICCOB.S. equivalent certificate, Politecnico di Torino, 2000

THESIS

Submitted as partial fulfillment of the requirementsfor the degree of Master of Science in Electrical Engineering and Computer Science

in the Graduate College of theUniversity of Illinois at Chicago, 2002

Chicago, Illinois

This thesis is dedicated to my parents, Maria Teresa Allione and Giovanni Picco: without

their support and suggestions it would never have been accomplished.

iii

ACKNOWLEDGMENTS

I would like to acknowledge all the people who have helped me during my studies, in

particular the Telecommunication Networks Group of the Turin Polytechnic that gave me the

opportunity to develop this work with an high degree of autonomy. A special thank to my

thesis advisor, professor Fabio Neri, and the people who guided me through the work: Emilio

Leonardi, Andrea Bianco and Maurizio M. Munafo.

WP

iv

TABLE OF CONTENTS

CHAPTER PAGE

1 OPTICAL NETWORKS TODAY . . . . . . . . . . . . . . . . . . . . . 11.1 From electronics to optics . . . . . . . . . . . . . . . . . . . . . . 11.2 Optics advantages and disadvantages . . . . . . . . . . . . . . . 21.2.1 The optical fiber . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.2.2 Components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61.2.3 Storing data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71.2.4 Processing data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81.2.5 Fault management . . . . . . . . . . . . . . . . . . . . . . . . . . 91.2.6 Contention resolution . . . . . . . . . . . . . . . . . . . . . . . . 91.3 Today’s optical components . . . . . . . . . . . . . . . . . . . . 131.4 Point–to–point fiber based networks . . . . . . . . . . . . . . . 161.5 Second generation optical networks . . . . . . . . . . . . . . . . 171.5.1 Bandwidth resource . . . . . . . . . . . . . . . . . . . . . . . . . 171.5.2 Other requirements . . . . . . . . . . . . . . . . . . . . . . . . . . 181.5.3 Broadcast–and–select Wavelength Division Multiplexing net-

works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201.5.4 Wavelength routing networks . . . . . . . . . . . . . . . . . . . . 211.5.5 Packet–switched Wavelength Division Multiplexing networks . 231.5.6 Synchronization schemes . . . . . . . . . . . . . . . . . . . . . . 26

2 ENABLING TECHNOLOGIES . . . . . . . . . . . . . . . . . . . . . . 312.1 Optical switch design . . . . . . . . . . . . . . . . . . . . . . . . 312.1.1 All–optical switches . . . . . . . . . . . . . . . . . . . . . . . . . 322.1.2 Electronic based switches . . . . . . . . . . . . . . . . . . . . . . 342.1.3 Space and power . . . . . . . . . . . . . . . . . . . . . . . . . . . 352.1.4 Keys to Optical Packet Switching project . . . . . . . . . . . . 362.1.5 Wavelength Switched Packet Network project . . . . . . . . . . 382.1.6 Shared–Memory Optical Packet switch project . . . . . . . . . 412.1.7 Asynchronous Transfer Mode Optical Switching project . . . . 432.2 3R all–optical regeneration . . . . . . . . . . . . . . . . . . . . . 432.3 Fiber delay lines design . . . . . . . . . . . . . . . . . . . . . . . 462.4 Optical Burst Switching . . . . . . . . . . . . . . . . . . . . . . . 482.4.1 Burst generation . . . . . . . . . . . . . . . . . . . . . . . . . . . 492.4.2 Channel scheduling . . . . . . . . . . . . . . . . . . . . . . . . . . 512.4.3 Quality of Service support using Optical Burst Switching . . . 54

3 SIMON: AN ALL–OPTICAL NETWORKS SIMULATOR . . . . 58

v

TABLE OF CONTENTS (Continued)

CHAPTER PAGE

3.1 The starting simulator: CLASS . . . . . . . . . . . . . . . . . . 583.1.1 Network description . . . . . . . . . . . . . . . . . . . . . . . . . 593.1.2 The node structure . . . . . . . . . . . . . . . . . . . . . . . . . . 603.1.3 CLASS user types . . . . . . . . . . . . . . . . . . . . . . . . . . 613.1.4 Network links . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 643.1.5 Routing implementation . . . . . . . . . . . . . . . . . . . . . . . 653.1.6 Simulation accuracy . . . . . . . . . . . . . . . . . . . . . . . . . 663.2 CLASS modifications . . . . . . . . . . . . . . . . . . . . . . . . 673.2.1 Network description . . . . . . . . . . . . . . . . . . . . . . . . . 673.2.2 Packets generation . . . . . . . . . . . . . . . . . . . . . . . . . . 683.2.3 Node architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . 693.2.4 Routing implementation . . . . . . . . . . . . . . . . . . . . . . . 733.2.5 Fiber delay lines usage . . . . . . . . . . . . . . . . . . . . . . . . 763.2.6 Simulation accuracy . . . . . . . . . . . . . . . . . . . . . . . . . 78

4 OPTICAL NETWORKS DESIGN . . . . . . . . . . . . . . . . . . . . 804.1 General formulation of the optimization problem . . . . . . . . 804.2 Particularities of optical networks . . . . . . . . . . . . . . . . . 824.3 Optimization problem faced in this work . . . . . . . . . . . . . 854.3.1 Modeling the links . . . . . . . . . . . . . . . . . . . . . . . . . . 874.3.2 Modeling the network . . . . . . . . . . . . . . . . . . . . . . . . 914.4 Optimization algorithm . . . . . . . . . . . . . . . . . . . . . . . 934.4.1 Starting point . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 964.4.2 Iteration steps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 984.4.3 A note on the implementation . . . . . . . . . . . . . . . . . . . 1004.5 Optimization program . . . . . . . . . . . . . . . . . . . . . . . . 101

5 SIMULATIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1045.1 Network topologies . . . . . . . . . . . . . . . . . . . . . . . . . . 1045.2 Maximum recirculation times . . . . . . . . . . . . . . . . . . . . 1095.3 General backbone topology . . . . . . . . . . . . . . . . . . . . . 1105.3.1 Type one users . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1105.3.2 Simulation with ON/OFF users . . . . . . . . . . . . . . . . . . 1175.4 USA backbone network . . . . . . . . . . . . . . . . . . . . . . . 1175.5 Topology number five . . . . . . . . . . . . . . . . . . . . . . . . 119

6 CONCLUSIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123

APPENDICES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124Appendix A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125Appendix B . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128Appendix C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138

vi

TABLE OF CONTENTS (Continued)

CHAPTER PAGE

CITED LITERATURE . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149

VITA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155

vii

LIST OF TABLES

TABLE PAGE

I LISTING OF RC FILE . . . . . . . . . . . . . . . . . . . . . . . . . . . 130

II TOPOLOGY ONE: NUMBER OF CHANNEL PORTS AND NUM-BER OF FDLS OF THE NODES FOR DIFFERENT NETWORKLOADS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139

III TOPOLOGY ONE: NUMBER OF CHANNELS ON EACH LINKFOR DIFFERENT NETWORK LOADS. . . . . . . . . . . . . . . . . 140

IV TOPOLOGY FOUR: NUMBER OF CHANNEL PORTS AND NUM-BER OF FDLS OF THE NODES FOR DIFFERENT NETWORKLOADS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142

V TOPOLOGY FOUR: NUMBER OF CHANNELS ON EACH LINKFOR DIFFERENT NETWORK LOADS. . . . . . . . . . . . . . . . . 143

VI TOPOLOGY FIVE: NUMBER OF CHANNEL PORTS AND NUM-BER OF FDLS OF THE NODES FOR DIFFERENT NETWORKLOADS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146

VII TOPOLOGY FIVE: NUMBER OF CHANNELS ON EACH LINKFOR DIFFERENT NETWORK LOADS. . . . . . . . . . . . . . . . . 147

viii

LIST OF FIGURES

FIGURE PAGE

1 Erbium–doped fiber amplifier scheme. . . . . . . . . . . . . . . . . . . . . 14

2 A simple representation of a broadcast–and–select network. . . . . . . . 21

3 An example of three stages logarithmic delay lines structure. . . . . . . 28

4 Architecture of base–m AWG scheme. . . . . . . . . . . . . . . . . . . . . 29

5 Cascaded base–m AWG. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

6 The first scheme for the switching fabric proposed by the KEOPS project. 37

7 The second scheme for the switching fabric proposed by the KEOPSproject. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

8 The WASPNET switch architecture. . . . . . . . . . . . . . . . . . . . . . 40

9 The SMOP optical switch. . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

10 The ATMOS switch with fiber loop memory. . . . . . . . . . . . . . . . . 44

11 Scheme of FDL designed to obtain variable delays. . . . . . . . . . . . . 48

12 An example of LAUC scheduling. . . . . . . . . . . . . . . . . . . . . . . . 52

13 An example of LAUC–VF scheduling. . . . . . . . . . . . . . . . . . . . . 54

14 The node architecture that can be simulated with SIMON. . . . . . . . 70

15 An example of packet forwarding. . . . . . . . . . . . . . . . . . . . . . . . 76

16 The node model using queueing theory. . . . . . . . . . . . . . . . . . . . 89

17 Simple network example. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93

18 General backbone structure (topology number one) . . . . . . . . . . . . 105

ix

LIST OF FIGURES (Continued)

FIGURE PAGE

19 USA backbone structure (topology number four) . . . . . . . . . . . . . . 106

20 Topology number five . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107

21 Throughput of topology one in the case of different maximum recircula-tion times. All the links have two channels. . . . . . . . . . . . . . . . . . 110

22 Delay of topology one in the case of different maximum recirculationtimes. All the links have two channels. . . . . . . . . . . . . . . . . . . . . 111

23 Throughput of basic configuration. . . . . . . . . . . . . . . . . . . . . . . 113

24 Delay of basic configuration. . . . . . . . . . . . . . . . . . . . . . . . . . . 114

25 Throughput of topology one with different number of users. All the linkshave four channels. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115

26 Delay of topology one with different number of users. All the links havefour channels. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116

27 Throughput of topology four, one user for each node. . . . . . . . . . . . 118

28 Delay of topology four, one user for each node. . . . . . . . . . . . . . . . 119

29 Throughput of topology four, one user for each node at low network loads. 120

30 Throughput of topology five. . . . . . . . . . . . . . . . . . . . . . . . . . . 121

31 Delay of topology five. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122

32 Optical component representation, part one. . . . . . . . . . . . . . . . . 126

33 Optical component representation, part two. . . . . . . . . . . . . . . . . 127

x

LIST OF ABBREVIATIONS

ACTS Advanced Communications Technology and Ser-

vices

ATMOS Asynchronous Transfer Mode Optical Switching

AWG(s) Arrayed Waveguide Grating(s)

BER Bit Error Rate

bps bit per second

BSPS Bit–Sequential Packet–Switching

CG–SOA(s) Clamped–Gain Semiconductor Optical Amplifiers

CLASS ConnectionLess ATM Services Simulator

CHP(s) Control Header Packet(s)

CSELT Centro Studi E Laboratori Telecomunicazioni

EDFA(s) Erbium–Doped Fiber Amplifier(s)

FBG(s) Fiber Bragg Grating(s)

FDL(s) Fiber Delay Lines(s)

GMPLS Generalized Multi–Protocol Label Switching

KEOPS KEys to Optical Packet Switching

LAN Local Area Network

xi

LIST OF ABBREVIATIONS (Continued)

LAUC Latest Available Unscheduled Channel

LAUC–VF Latest Available Unused Channel with Void Fill-

ing

LED(s) Light Emitting Diode(s)

MAC Media Access Control

MEMs Micro Electro Mechanical Systems

MLM Multi–Longitudinal Mode

MPLS Multi–Protocol Label Switching

MZI Mach–Zehnder Interferometer

OBS Optical Burst Switching

PDH Plesiochronous Digital Hierarchy

RACE Research and Development in Advance Commu-

nications in Europe

RAM Random Access Memory

SCM SubCarrier Multiplexing

SDH Synchronous Digital Hierarchy

SIMON SIMulator for Optical Networks

SOA(s) Semiconductor Optical Amplifier(s)

SONET Synchronous Optical NETwork

xii

LIST OF ABBREVIATIONS (Continued)

SMOP Shared–Memory Optical Packet

TWC Tunable Wavelength Converter(s)

VCI(s) Virtual Circuit Identifier(s)

WASPNET WAvelength Switched Packet NETwork

WDM Wavelength Division Multiplexing

xiii

SUMMARY

Optics can introduce new opportunities in the information exchange infrastructure: since

transmission and switching bandwidth represents the most precious resource for networks to-

day, the introduction of the optical technology is seen as the most powerful way of evolution

for the communication world. Thus huge efforts are spent today from the scientific community

to change the way data are processed, stored and transported in telecommunication networks,

allowing the use of light in many network layers. One of the major drawbacks in this rad-

ical revolution is the difficulty of processing and storing data in the optical domain, due to

the lack of suitable components in the current technology: for this reason, the first types of

optical networks adopted circuit switching techniques to overcome these problems. Only re-

cently researchers have focussed their attention to pure optical packet switching methods: in

this way new protocols can be studied to exploit the potentials of optical networks. Due to

the changes in architectures and protocols driving information processing inside the nodes, all–

optical networks must be studied with properly designed tools, capable to deal with many new

characteristics.

In this thesis a new simulator, designed ad–hoc for optical networks, was developed: it

can deal with the new architectures designed to manage optical signals, and it is topology

independent: this features ensures a great flexibility and the maximum adaptability of this

kind of program, making it a useful tool in most situations of interest.

xiv

SUMMARY (Continued)

If new architectures and protocols are used in a network, then the aspects of topological

design have to be reconsidered taking into account the specific features introduced by optics:

the allocation of resources can affect heavily the overall performance of a network, so that

designers have to adapt the optimization criteria to the new framework. In this work, a model

of the possible architectures for next generation all–optical networks is studied, allowing to face

the problem of the optimum allocation of resources among all network elements to obtain the

best performances from the network being designed.

The first chapter of this thesis is devoted to the description of the current state–of–the–art in

the optical network framework: the reasons of the interest for optical technologies are discussed,

and the main advantages and disadvantages of this revolution are outlined. The development

of signal transmission methods in optical fibers and in the basic network components (such

as transmitters, receivers, amplifiers) are briefly described, together with the architectures of

point–to–point fiber based networks. The main innovations introduced by second generation

optical networks conclude the chapter, outlining the main differences between broadcast–and–

select and packet–switched wavelength division multiplexing network types.

The second chapter reports the technologies that are currently developed by the researchers:

the results of a number of studies about switch design are summarized, and the latest advances

in the all–optical regeneration process are exposed. The problems concerning the design of

fiber delay lines, and possible ways to solve them, are also reported. Finally a new technique,

called optical burst switching, that is currently object of interest, is described, outlining the

xv

SUMMARY (Continued)

advantages and the requirements that can be obtained by its use in future generation optical

networks.

The third chapter describes the features of SIMON, a simulator of all–optical networks

developed in this thesis. A description of CLASS, the simulator from which SIMON was

obtained, is given in the first part of the chapter: in this way a global picture of the simulator

features can be achieved easily. The way of describing the network topology to the simulator

is described, allowing the reader to understand the SIMON usage in short passages; a detailed

list of the types of connections, and of the packets generators is also given in this chapter. The

different node architectures supported by the simulator are shown, together with the routing

algorithm implemented by the simulator. Some aspects related to the simulation accuracy are

exposed, to make clear the statistical significance of the results obtained with the software.

The fourth chapter faces the problem of the optimum design of an optical network. The

distribution of a certain amount of resources is a well known question, deeply studied for the

traditional electronic–based networks. In the case of optical structures, new considerations can

be done, and some aspects of the classical approach must be reviewed. In this chapter the

model for the new node architectures developed in the thesis is described, together with an

heuristic approach to the optimization problem.

The fifth chapter reports some of the results obtained through this work: the algorithm

developed to optimally allocate the available resources is applied to some topologies — chosen

for the importance that they have in the current network framework —, and the obtained

solutions (i.e. the obtained network architectures) are simulated using SIMON. In this way the

xvi

SUMMARY (Continued)

effectiveness of the optimization algorithm can be verified, and the behavior of the simulator

too.

The last chapter reports the general conclusions and summarizes the obtained results.

xvii

CHAPTER 1

OPTICAL NETWORKS TODAY

This chapter introduces the topic of the optical networks, the related problems and the

solutions adopted in the current state of the art.

1.1 From electronics to optics

Human beings need to communicate between each other in all the situations. The current

network technology offers solutions to satisfy a part of this necessity, and its characteristics of

simplicity and flexibility make the use of the existent structures wider and wider every day.

This network usage comes out in an increasing demand of bandwidth, that is growing very

fast: new applications like the Internet, multimedia, or advanced digital services need high

resources from the network. Looking inside in the different solutions adopted by the Internet

architectures through the years, it’s possible to see how electronic devices were modified to

allow better performances to process data. Router design, for example, is increasing the speed

of data processing in a very effective way.

However forecasts on the traffic demand in the next years are not optimistic: electronics has

many physical limitations that can’t be overcome with new materials or more accurate designs.

This is due to the elementary particle used to manipulate data in all the today devices: the

electron. To increase the performances VLSI designers have reduced the dimensions of the

elementary components through the years, and now some parts are only ten times bigger than

1

2

the electron, or even less; moreover the speed of electrons in all the physical supports can’t be

increased over some thresholds. These observations induce the will to operate a big revolution in

the way to carry, store and process data, choosing alternative solutions to electronics. The way

that today the scientific community is tracing is to replace the electron with another element:

the photon.

1.2 Optics advantages and disadvantages

Radical changes are difficult to be implemented in almost all the technology fields: this is

the cost that must be paid for the progress of science. In the particular case of the passage

from electronics to optics the revolution starts from the bottom of the network architecture,

and so it is not immediate to be implemented. The problems that must be solved during this

transformation are discussed here, and the obtained advantages also, analyzing many aspects

of the subject.

1.2.1 The optical fiber

The optical fiber is the elementary component that represents the physical medium in which

light pulses can travel. It plays one of the most important roles of the information exchange:

the data transport.

Wide opportunities are given today by fibers: a single glass guide can substitute hundreds

of copper wires or coaxial cables in term of carried traffic. Taking into account that there is

the possibility to put many fibers in a single cable (commonly some dozens), and that many

wavelengths can be accommodated on each fiber, it is simple to understand that a two inches

cable can carry the peak traffic of the telephone backbone network in the USA. Bandwidth is in

3

general the most searched and precious resource today, and optics represents a very attractive

way from this point of view: the light seems to have the intrinsic property to operate with high

bandwidths without problems. An illustrative example is given by the prism: it is a natural

optical switch that doesn’t care about the amount of bandwidth it has to deal with.

The opportunity to fit many wavelengths on a single fiber provides a simple way for het-

erogeneous users to share the network resources. For example certain wavelengths could carry

analog signals, while other wavelengths could be used for digital applications at the same time;

moreover, different modulation formats can be used by different network terminals that are

independent from the network core: they may be upgraded without any network reconfigura-

tion. As a philosophy, the network will provide only the basic resource (bandwidth) on demand

and let the users determine their individual hardware requirements: this is referred to be the

network transparency.

Another characteristic that makes interesting the use of optical fibers is that there isn’t the

necessity to regenerate the signal after few kilometers as needed using the common copper wires,

and the distance between two regenerators can be significantly increased; it is possible to pose

two consecutive signal regenerators after some hundreds of kilometers — the distance varies as

function of many factors, like bit–rate and modulation —. This represents a big advantage in

terms of maintenance: it is made simpler and faster. While in the today networks the signal

needs to be amplified because it is strongly attenuated after a few dozens of kilometers, in the

case of optics the signal is regenerated to attenuate the undesirable effects of the transmission

on a fiber. Some of these effects are described in the following of this section.

4

Today the copper wires, and in general all the transmission cables, are quite sensible to

the electromagnetic fields that can disturb the data. So a part of the cost in the traditional

transmission systems is dedicated to shield properly the signal from external disturbs; on the

other hand the intrinsic characteristics of the optical signal make it very robust with respect

to other ones outside the fiber. Moreover fibers have a low cost for unit of length, they are

flexible and easy to be managed with respect to traditional cables; all these features makes

optical fibers the optimal candidate to replace wires in the metropolitan areas.

To carry data on optical fibers is a well studied problem: it is shown how rays of light on

different wavelengths can travel together without many attenuations in well designed fibers:

this is called the Wavelength Division Multiplexing (WDM) technique; a bandwidth of many

TeraHertz can be obtained easily on a single fiber. One of its interesting characteristics is

represented by the facility in the addition of new bandwidth on an existing structure: one more

wavelength channel on a fiber implies that the changes in the network are performed only to

the nodes architecture (that must be allowed to process the data on the new wavelength), and

no new infrastructures need to be posed, nor the bit–rate is increased.

But there are new problems due to polarization and modulation of the signal, and to the

fact that high bit–rates and long distances make stronger the effects of dispersion. One kind of

dispersion is due by the different modes of propagation of the wave of light inside the fiber: the

same data can take different ’ways’ inside the fiber core, leading to different propagation times

and a modification of the received signal. This problem can be overcome using single–mode

fibers, in which the light must choose a single path to propagate inside the medium. Another

5

kind of dispersion id the so called chromatic dispersion, given by the fact that the diffraction

index of a fiber is a function of the light pulse frequency; in addition, the path followed by a pulse

depends from its frequency also. This means that pulses with different wavelengths travel at

different speeds in the fiber; this problem can be solved using dispersion–shifted fibers: they are

opportunely modified to limit the bad effects of the chromatic dispersion in the used frequency

region.

Other non–linear effects can create new signals inside the fiber: this is made possible by the

mutual interaction between the different signals themselves. One of these undesirable effects

is the self–phase modulation: signals having different power can experience different delay

propagations; the direct consequence is that the signals can change frequency — a phenomenon

called chirping — during transmission. The frequency shift depends from the signal level, so

different channels traveling on the same fiber can amplify the problem and induce different

chirpings on the different wavelengths: this aspect of the problem is classified as cross–phase

modulation. This two phenomena strongly depend from the working bit–rate in the fiber.

Another non–linear effect that rises sending pulses on a fiber is the so called four–wave

mixing. This effect produces new signals inside the fiber, with a wavelength that is a linear

combination of the existing ones. The difference of this latter problem with respect to the

two previously exposed is that it does not depend from the bit–rate, but from the spacing of

the channels and the intrinsic dispersion of the fiber. It is useful to notice that in general the

non–linear effects induced by transmission in the optical fibers depend from the signal power.

6

The described non–linear effects seem to be a limitation of the optical domain. This is in part

true: these phenomena limit the distance between two amplifiers, and make complicated the

design of the network elements, such as amplifiers, in which these effects must be kept as low as

possible; but they can also be exploited in ad–hoc systems: a simple example is represented by

the wavelength converters, in which the wave mixing, the cross–phase or cross–gain modulation

are used in a proper way to convert the signal.

1.2.2 Components

One of the initial problems is that all the network basic components — such as transmitters,

receivers and amplifiers — must be completely changed. This implies that the people who are

going to design the new networks must know very well all the aspects of these new devices, in

terms of performances, way of working and limitations. A second drawback is represented by

the high costs of the optical devices: while optical fibers — with respect to the usual cables —

are convenient, here the situation is completely different. The limited diffusion of the optical

devices, and the fact that they are currently object of extensive studies (since optics is a quite

new subject), contribute to maintain high the costs of almost all the devices.

One advantage of these components is represented by their very long life: the probability to

get a component broken during the work is many orders of magnitude below the threshold for

electronic hardware. This means that in front of high investments to create a new network, the

maintenance of the installed systems does not require much effort, giving high performances

for a long time.

7

1.2.3 Storing data

The problem of storing data is in the optical domain is still open (Zhong and Tucker, 1008).

One of the main differences between the optical environment and the electronic one is that in

the first scenario there is not the possibility to memorize data in a simple way. In fact the

optical RAM is not ready yet to be used (it is only in the experimental stage), and so the very

large bandwidth offered by the optical fibers is difficult to be used without any optoelectronic

conversion of the data. To memorize packets is a very important task because it is the most

common way to solve contentions in a network node: if two packets must be forwarded on the

same output link (supposed free) one is memorized in the node buffer, and the other is sent

immediately on the link.

A packet can be delayed for a certain period of time in the optical domain without any

conversion using FDLs (Fiber Delay Lines: portions of optical fibers with a certain length),

and this method is the best the technology offers today to memorize packets in optics. The

main difference between FDLs and RAM can be viewed as the difference between continuous

and discrete time domains: the FDL can make available the packet for transmission only at

certain moments (when the packet reaches the opposite side of the FDL), while the data stored

in a RAM memory can be taken at each instant after the memorization. Another limitation

is represented by the times a packet can be put an a FDL consecutively: in fact the signal to

noise ratio of the signal decreases for each recirculation; this means that after a while the data

need to be regenerated, unless they are not usable by the following stages. Having this in mind,

it’s simple to see that the FDL’s length is a measure of the granularity of the storage capacity:

8

longer FDLs give a longer recovery time, but less flexibility; on the other hand shorter FDLs

give more opportunities to have a packet ready to be transmitted, but noise problems come out

in a serious way. When long delays are needed, the most common solution should be to use

a long FDL to minimize the number of recirculations and consequently the signal noise; this

solution has an intrinsic limitation in this framework because, in spite of the high velocity the

data have in the optical fibers, a 20 centimeters FDL can give only a delay of one nanosecond;

this poses an upper bound to the achievable delay.

There are recent studies that show how to design FDLs to obtain high quality performances:

they concern both the length of a FDL both its physical implementation; see section 2.3 for

details on the results.

1.2.4 Processing data

There are currently no commercially available devices that can analyze data without opto–

electronic conversion. This impose a severe limitation in the performances of optical networks.

A solution that is currently at the study is to convert only the data that must be processed

— represented by the packets headers in the packet switched networks, for example —, and to

bypass transparently all the other ones (the payload).

To maximize the bandwidth utilization the payload transmission time should be equal to the

header processing time: in fact if this condition is satisfied the physical mediums are exploited

in all the instants. However in many situations, due to the limited speed of data processing

in the electronic domain, the time needed to process a single header results longer than the

transmission time of the respective payload. To avoid waste of resources many packets with the

9

same destination, or any other common feature, can be assembled at the network edge, before

entering the region controlled by the all–optical switches; this approach is being studied by

many researchers today, and it is referred to be the Optical Burst Switching (OBS) technique,

which is exposed with more detail in section 2.4.

1.2.5 Fault management

Network engineers must take in account the event of a fault on a link, and the consequences

that this fact can have to the network performances. In general an accurate network design

must provide a new connection for each broken one. The optical layer faults — generally

caused by broken fibers, or by nodes equipment failures — can be efficiently overcome, with

a restoration time of few milliseconds. To provide alternative connections the network must

have a certain redundancy degree, and the redundancy is typical of the fibers: if not all the

wavelengths available on a fiber are used, these free channels can be used in the case another

fiber of the cable breaks itself.

1.2.6 Contention resolution

Contentions come in the facts in the network switches when two or more packets have to

exploit the same resources: for example when two packets must be forwarded to the same

output channel at the same time. This is a consequence of the nodes architecture and the

packet–switched way of working: in this framework there is the possibility that two or more

packets have to be forwarded to the same output link simultaneously; a second situation where

contentions come up is when the node has to forward only one packet to a certain output port,

but there is an already started transmission at the specific moment.

10

The adopted solutions to solve these contentions are a key aspect in packet–switched net-

works, and they can affect heavily the overall network performance. The optical domain offers

new ways to solve contentions, but also makes difficult to implement the usual ones.

The simplest solution to overcome the contention problem is to delay the transmissions of

the packets that collide with other ones, exploiting the time domain. This operation assumes

the availability of efficient memories, capable to store the information for a variable amount of

time, and with little access time: a component with these characteristics is not ready in the

optical domain yet. This problem can be partially solved using fiber delay lines, because the

action to delay a packet enters in the more general problem to store data, previously exposed

(see section 1.2.3) and analyze them: in fact a payload is delayed when the node has to process

the packet header in the forwarding process. Optical buffering can offer high throughput but

needs many controllers and heavy hardware to be implemented. For this reason optical networks

try to keep as low as possible the employed buffer quantity; this task can be achieved using

slotted networks, with fixed length packets: in this framework the switches can operate at the

link speed, and no output buffering is needed because the packets can’t arrive while another one

is being transmitted. The only situation in which contention can occur is when two packets,

from different input channels, have to be routed on the same output channel.

Optical domain makes difficult the simplest way to solve contentions, but in the same time

it makes possible to implement a new strategy: wavelength conversion. This new possibility

is typical and unique of optics, which inserts the new wavelength domain adding it to the

two already existing ones: time and space. The switch exploiting this solution have to detect

11

the possible contentions on the nodes and to provide the proper wavelength conversion to the

packets that are going to collide; then the packets would be sent to the same output port and

transmitted at the same time after having multiplexed them on the single link. This solution

can be adopted in wavelength routing networks, where the available bandwidth on a single

fiber is divided in different channels (see section 1.5 for further details). With the available

wavelength converters is possible to obtain a noise–free and reshaped converted signal, which

in turn lowers the bit error probability of the network. Clearly the effectiveness of this solution

depends from the number of wavelengths on each channel, and from the number of converters

embedded in a single node.

But there are also other solutions different from packet storing to solve contentions. A

possible way — called deflection routing and sometimes hot–potato routing (Yao et al., 2000;

Morino et al., 2000) — is to change the output port to which the packet is sent, therefore to

exploit the space domain in the network: this implies that the optimal routing is changed, and

packets can take longer ways to reach the destination, suffering high delays; in addition, the

packets may arrive to the destination in a different order with respect to the initial sequence: this

fact must be taken into account designing the receiver. When deflection routing is implemented

in a bufferless network the packets queuing delay is absent, but the propagation delay is larger

than in the buffer solution, due to the longer routes the packets are allowed to run through:

there is no possibility to detect the better solution for a given network, unless simulation is

performed.

12

The most important advantage of this method is that it does not require huge efforts to be

implemented, neither in the hardware implementation, nor from the algorithmic point of view

(Fayoumi et al., 2000). Specific studies (Zang et al., 2000) on mesh interconnected networks

has shown that if the network load is not large the performances given by a system completely

governed by deflection routing are quite similar to the results given by the buffer approach. On

the contrary the first solution is better at high loads: in fact deflection routing can overcome

the unbalance in the links load produced by a fixed short–path routing, spreading packets

in all the network. The results obtained with deflection routing depend also from the time

division adopted in the network: in an unslotted framework the minimization of the number of

deflections in a single switch is shown (Chich et al., 2001) to be an NP–complete problem, while

in the slotted ones it can be easily solved. Nevertheless deflection routing can be adopted in a

useful way in unslotted networks also, because many heuristic solutions permitting to minimize

the number of deflections can be found. The network topology heavily affect the throughput

performance of this type of routing: the network diameter — the largest number of nodes

among all the shortest paths in the network — and the deflection index — the number of nodes

added to the shortest path by a single deflection — play an important role in the overall result.

It can be easily understood that networks with small diameters and small deflection indexes

obtain good results with deflection routing. Moreover a further rising of the throughput can

be obtained with clever deflection rules, in other words how to choose the packet that has to

be deflected and the respective output port. A simple and effective deflection rule is to choose

randomly among all the packets that contends an output port; alternatively, the so called

13

’closest–to–finish’ solution forwards correctly the packet that is nearest to its destination: this

implies that a specific information (hops to the end) must be stored in the packet header and

analyzed by the node.

A mixed approach that uses more than one contention resolution method can also be per-

formed: for example wavelength conversion can be associated with deflection routing (Chich

and Clerot, 2000). In this case the network complexity is not high and the performances are

quite good both in the synchronous, both in the asynchronous case.

1.3 Today’s optical components

Without components able to manage signals in optical form nobody can hope to exploit the

enormous potential of optical fibers.

Light Emitting Diodes (LEDs) were used in the past to transmit the signal over a multimode

fiber. The signal obtained in this way has a large spectrum, so phenomena like chromatic dis-

persion and other non–linear effects force the necessity to regenerate the signal after a relatively

short distance. Moreover regeneration was obtained converting the signal into the electronic

domain, and then retransmitting a new light pulse: this fact breaks the transparency of the

network and makes difficult to threat with high capacities, due to electronics limitations.

Advantages were obtained using monomodal fibers and Multi–Longitudinal Mode (MLM)

lasers: in this way the signal is stable, coherent and with an extremely narrow bandwidth. This

improvement in technology let to allow longer distances between two regeneration stations. The

further step was the adoption of the optical amplifier, that is capable to amplify a signal in the

optical domain. Also in this field the performances grow very fast: today high–quality amplifiers

14

EDFA

PUMP RESIDUAL PUMP

IN OUT

Figure 1. Erbium–doped fiber amplifier scheme.

can give flat gains and low noise insertion; in addition the total power given at the output is

increased of many order of magnitude with respect to the first generations amplifiers. Today

there are two main types of optical amplifiers: Erbium–Doped Fiber Amplifiers (EDFA) and

Semiconductor Optical Amplifiers (SOA). The first is the more used because it is very reliable

and simple in its structure, as it can be seen in Figure 1: it is composed by a wavelength–

selective coupler around the doped fiber that combines the input signal with the pump laser

signal, another coupler before the output that purge the remaining pump signal, and an isolator

at the output (see appendix A for all the schematic representations of the optical components

shown in the figures). Moreover EDFAs introduce no crosstalk in the signal and are polarization

independent. SOA gates are not so good as EDFA as amplifiers, but are used with other devices

as basic components: space switches, wavelength converters and wavelength selectors can be

made using SOAs, producing best–quality performances.

15

Wavelength converters can be obtained exploiting the dependence of the gain in a SOA

by the input signal power (cross–gain modulation effect): the SOA can be driven by a probe

signal, and the input is converted at the probe wavelength. With this method the probe powered

signal must be filtered at the output, and the input signal must be sufficiently high to have

a good performance by the SOA. Another way to convert the wavelength of a signal is to use

the cross–phase modulation: the phase modulation induced in the SOA by the variation of the

carrier density can be converted into intensity modulation using a Mach–Zehnder interferometer

(MZI), with the couplers in asymmetric coupling ratio configuration. In this case less power

is demanded at the input to obtain the same performances with respect to the cross–phase

modulation method. There is also another possibility in the wavelength converters design:

wave–mixing phenomenon can be enhanced in SOA by high input power. Putting a probe

signal such that the produced frequencies are inside the SOA bandwidth, and filtering the

output, the desired conversion can be obtained. This method is completely transparent, and

independent by the bit–rate, but it has a low efficiency: in fact only one component of the

produced signal is sent to the output.

Space switches can use SOAs to pass single wavelengths to the output ports; the currently

proposed node architectures are described in section 2.1.1.

To implement filters in optical domain is not so immediate. Fiber Bragg Gratings (FBGs)

can select a specific wavelength from a fiber, allowing to split the different colors of the signal:

it is obtained modifying a small section of fiber to make change its refraction index in a periodic

way. Therefore only the Bragg resonance wavelength can pass thought this piece of fiber, while

16

all the other components will be reflected. FBG is used to build basic components of the

network, such as the wavelengths add/drop multiplexers, used to combine multiple wavelengths

onto a single fiber.

1.4 Point–to–point fiber based networks

The first modification to the existent networks was performed on the physical layer. The

low cost of the optical fiber and the tremendous increase of bandwidth obtained with this

new technology convinced the network engineers to invest in the new optical devices — such

as transmitter, receivers, and amplifiers — forecasting an improvement of performances at

acceptable costs. In this way after 1980 if a backbone cable would be posed, it probably

would be a group of optical fibers. The upper layers of the network would be the same of the

previous generation: all the data are transmitted point–to–point in optical form on the links,

but at each node an opto–electronic conversion is performed and the data are analyzed with the

usual devices; then they would return in optical form on the next link. For this reason these

networks are referred to have a single wavelength. Examples of such networks are represented

by Synchronous Optical NETworks (SONET) and Synchronous Digital Hierarchy (SDH) used

in North America and Europe backbones, respectively.

SONET is one of the most important standards regarding line rates, signal transmission

and shaping, coding schemes. SONET defines multiplexing techniques very different from PDH

(Plesiochronous Digital Hierarchy, the previous standard), assuming that all the networks el-

ements are synchronized to a master clock: its principles allow to have significant increase in

network reliability and, at the same time, reduction in equipment requirements. It defines also

17

the type of network elements, simplifies the interface to digital switches and fixes some param-

eters for their basic functionality: this makes easier the connection of elements from different

vendors; moreover this standard includes topologies and schemes for network management to

allow huge performance services. One SONET characteristic is its flexibility: it defines struc-

tures and schemes in such a way to allow future services to be implemented without big changes

in the whole network infrastructure.

1.5 Second generation optical networks

A network exploiting the intrinsic characteristics of the optical domain not only for the

point–to–point transmission, but also for other functions — as the routing process —, is referred

to belong to the second generation optical networks: in this case the optical revolution enters

more in depth in the architecture, affecting more than one network layer.

1.5.1 Bandwidth resource

Many carriers found that their estimates of bandwidth requirements had been highly under-

estimated: this is due principally to the exponential growth in communications, caused mainly

by the wide acceptance of the Internet, as previously explained (one illustrative example is

the World Wide Web growing); moreover, electronic banking and the cost declining of home

computers have expanded the number of network users in the society. When posed, most cables

included many unused fibers, but this growth has used many of them already and new capacity

is needed. Three methods exist for expanding capacity: installing more cables, increasing sys-

tem bit–rate to multiplex more signals, and finally wavelength division multiplexing (WDM)

technique.

18

The first solution (installing more cables) will be the preferred method in many cases,

especially in metropolitan areas: optical fibers are incredibly inexpensive media compared with

the bandwidth performances, and installation methods have become more and more efficient

within the years. But if new space is not available, or massive constructions are necessary —

introducing prohibitive costs —, this may not represent a viable solution. The second solution

(increasing system bit–rate) may be more complicated either than the previous one: electronics

are highly sensitive to the bit–rate, and modifying it could let the consequence to change all

the electronic devices in a network. This change is not always possible, given the extremely

high bandwidth offered by the fibers — the actual bottleneck in end–to–end data transmission

is now represented by electronics —, and when it is possible it has extremely high costs.

The third alternative, WDM, has been proved to be the more cost effective in many instances

of interest (Mukherjee, 1997): it allows the use of the already posed fibers, and with little

changes inside the network nodes it can simply transmit different data channels at different

wavelengths in a shared fiber. The systems that already use optical amplifiers as repeaters also

do not require any upgrading: these devices are frequency independent.

1.5.2 Other requirements

Of course an optical network must provide other characteristics beyond high bandwidth

capacity: it must be robust, flexible, and cost efficient. The increasing in the number of

network users has the natural consequence to make growing the demand for integration of

widely heterogeneous communications services. For this reason the new networks must be

designed in such a way to be flexible enough to provide a wide variety of different services;

19

having to deal with varying bit–rates and modulation schemes, the network must be designed

in a backward compatible way with existing applications and local networks, and also forward

compatible with future services. Another aspect of the network flexibility is the ability to

provide a certain quality of service — concerning delay, packet loss, and security —, depending

to the service associated to a network flow. Moreover such a network must provide low bit

error rates, and it has to be robust enough to deal with ticklish applications (remote banking

operations, for example), and finally be easily upgradable. But if all these aspects are not

joined with a relative cost effectiveness, such a network will never be of practical use.

WDM networks meet these requirements. In addition they are intrinsic natural solutions to

provide integrated services: putting different data channels on fibers, the bandwidth of WDM

networks is naturally divided into independent, distinct, and non–overlapping channels, each

receiving a different treatment; for example a different priority can be reserved for each specific

channel in the network nodes. It is also possible to reserve more channels for any single service:

the granularity of the bandwidth that can be offered to a service depends from the bit–rate of

each channel, while the total number of channels depends of course by their frequency distance.

WDM networks can be divided into two major categories: circuit–switched and packet–

switched ones. The distinction lies in the way the flows are threaded in the network: in the first

case the resource reservation is made from the source to the destination (end–to–end basis),

while in the second case it is limited to a single link (hop–by–hop basis). These two ways of

working let to very different networks architectures, that are briefly described in the following.

20

1.5.3 Broadcast–and–select Wavelength Division Multiplexing networks

The simplest type of circuit–switched WDM networks is the broadcast–and–select network.

The key components of such a network are the central passive star coupler — all the end–users

are connected to it — and the set of tunable receivers, one for each user; a single distinct

transmission wavelength is assigned to each user; a simple design of such a network can be

seen in Figure 2. The sender transmits at its fixed wavelength, and the passive star coupler

broadcasts this signal to all of the other end–users in the network: this task can be achieved

using a set of couplers that split the signal to all the star coupler output ports. A Media

Access Control (MAC) protocol is needed when one user wishes to establish a transmission

with another one in the network: in fact the destination user must tune its receiver accordingly

to the transmission wavelength of the sender. Therefore an end–user can receive only one

transmission at a time, since it is provided on an unique tunable receiver: MAC protocols must

minimize the number of colliding transmissions (directed to the same receiver) to maximize the

network performance. It is also possible to design a broadcast–and–select network exploiting

the symmetric principle: the reception wavelengths are fixed while tunable transmitters change

wavelength accordingly to the end–user that they have to reach. This second way doesn’t

change the general mode of operation in the network that remains the same.

This type of network is very simple to control and is extremely reliable: in fact both the op-

tical links and the star coupler are completely passive and unpowered, that means management

facilitation. But many problems arise from this structure: first, the transmission signal must

have a larger power than strictly required since it has to be divided evenly between all of the

21

Figure 2. A simple representation of a broadcast–and–select network.

receivers in the network: this leads to an unnecessary dissipation of the optical power. Second,

a distinct wavelength is assigned to each different user, so the number of available channels lim-

its the number of users: for this reason broadcast–and–select networks are not easily scalable.

This is the price that must be paid in order to not have any active routing in the network.

1.5.4 Wavelength routing networks

This is the second type of circuit–switched WDM network. In these networks routing is

based on the data wavelength, which is considered as an intrinsic routing information: wave-

length routing provides a unique transparent light path between network terminals. A light

path can be viewed as the extension of the point–to–point transmission on a single link: it is a

22

path an optical signal traverses from a source to a specific destination in the network, and of

course it can count many different links (Srinivasan and Somani, 2001).

In this way there is the elimination of the divisions of the signal power. Moreover a single

wavelength can be spatially re–used in multiple, non–overlapping parts of the network (Ra-

maswami and Sivarajan, 1996; Ramaswami and Sivarajan, 1995). In the simplest case the

routing is fixed: a certain wavelength is always routed to a certain output port, with no ex-

ception. Therefore no configurable optical devices such as switches are required: the network

performs only a kind of ’passive’ routing, which remains fixed; the reliability of this kind of

networks is comparable with the broadcast–and–select ones. But a big problem that arises in

this configuration is to establish new lightpaths: with fixed routing only the existent lightpaths

can be effectively propagated through the network. The solution is to make capable the net-

work routers of dynamic reconfiguration — to change the destination of a certain lightpath

—: in this way a greater flexibility to a changing user environment can be provided, without

the use of any wavelength converter. Such wavelength routed networks are more complex than

their broadcast–and–select counterparts ones because they require the use more sophisticated

controllers capable to configure the routers: now new wavelengths can be added onto some

channels, therefore each node have to maintain a variable routing table.

The further step that can be performed to achieve the maximum scalability in a wavelength

routed network environment is to use wavelength converters at the routers in order to enable the

same connection to pass through different links using different wavelengths (Subramaniam et

al., 1996). Such an arrangement would allow to fit the maximum number of connections on the

23

limited available channels. Each node can obtain the routing information from the wavelength

as in the previous cases: for a certain node a wavelength represents a connection that must

be switched accordingly to a routing table. An analogy can be found in the reuse of the finite

number of distinct Virtual Circuit Identifiers (VCIs) in ATM networks. With this technique

the number of simultaneous connections that can be supported by the network is increased. Of

course optical wavelength converters with high performances are needed, to obtain a converted

low–noise signal in a short period of time: this increases furthermore the network hardware

complexity (Karasan and Ayanoglu, 1998).

1.5.5 Packet–switched Wavelength Division Multiplexing networks

The networks described in the previous sections use the data wavelength to guide the routing

decision; supposing that the incoming wavelength is independent from the data type, and from

the routing decision, a new concept of network can be studied.

If the wavelength does not represent any useful information for the forwarding process,

the switches must determine where to send the packet analyzing the header information. Any

other information — such as the payload — does not need to be processed by the switches,

and it can pass transparently through the device. To process the header there is the needing

to determine its starting instant in the optical domain: this task must be obtained in different

ways depending if the network is slotted or unslotted type (see section 1.5.6), but it depends also

from the method of header transmission. The simplest way, representing the direct derivative

of electronic packet–switching, is the optical Bit–Sequential Packet–Switching (BSPS): n bits,

usually by using on–off signaling, code the header for each packet on a given wavelength channel.

24

These header bits are analyzed by the switch control unit that prepares a suitable path into

the switch form the input to the designed output port; thus the payload can pass without any

processing through the switch. The header length depends from the number of addresses that

can be distinguished by the network.

The problem in this type of header transmission is the processing time of the switch: the

opto–electronic header conversion and routing table look–up are the current bottleneck in data

forwarding. In addition a certain guard time must be interposed between header and payload

to prevent the overlap of header and packet bits, due to negative effects such as fiber dispersion

and crosstalk that arise during transmission (Hunter and Andonovic, 2000).

The packet header can also be transmitted in a parallel way (bit–parallel packet switching):

separate channels within the same fiber link can be used for payload and header information,

thus enabling the payload and header to be transmitted in parallel instead of serially. Since the

header occupies the same duration of time as the data and may be processed in parallel, this

transmission technique can increase the network throughput.

The first bit–parallel coding technique is SubCarrier Multiplexing (SCM). It encodes the

payload at the baseband carrier frequency of the channel, while the header is encoded on a

properly chosen subcarrier frequency. This is possible because headers do not need the high

bit rate of the payload, and can tolerate the bit–rate limitation of the SCM sideband. The

advantage of SCM is represented by the efficient use of the available spectrum, but it is achieved

at the expense of bit–rate, that is constrained to be lower than the subcarrier frequency. In

conclusion SCM is useful when the available spectrum is limited and bit–rate is less of a concern.

25

The second technique is the multiwavelength bit–parallel packet–switching: it assigns differ-

ent optical wavelengths for header and payload. Thus, the packet occupies multiple wavelengths

as well as some duration in time; the division between payload and data channels becomes an

important factor. This technique is suitable for all–optical networks for different reasons: first

of all, it is simple to extract the header from the payload using simple passive optical filters; in

addition header coding may be adopted in the switches to run header recognition at the packet

rate instead of the data rate. Finally, there is no power penalty decoupling header and payload

sources, since separate sources are used for each wavelength.

However this scheme has some drawbacks: bit skew is an issue that needs to be taken into

consideration for any system that needs to synchronize multiple wavelengths. This phenomenon

is due to chromatic dispersion (see section 1.2.1) or delay variations for different wavelengths

in fiber: it can be taken into account using dispersion compensation, but this of course add

complication in the network design.

If the available bandwidth on the link becomes an issue, using a mixture of the first method

(SCM) and the second one (multiwavelength method) is also possible. But there are also

some other problems that must be considered in bit–parallel packet–switched networks: as

previously discussed, synchronization between wavelengths is a difficult issue whenever multiple

wavelengths — the header and the payload ones — need to be logically synchronized throughout

the routing process. Other problems such as crosstalk and signal degradation — and in general

all the aspects due to fiber nonlinearities, such as dispersion — are prominent players in the

performance of the network as wavelength channels become more closely spaced and the distance

26

between two consecutive repeaters increases. However with the actual technology progress in

the design of wavelength converters and stable optical amplifiers this method seems to be

affordable.

1.5.6 Synchronization schemes

Packet–switched optical networks can be classified in two main categories: slotted and

unslotted. The first type is represented by networks fitting exactly the packets in a defined

period of time; this implies that an upper bound on the packet length must be provided and

that a clock signal is passed through the network so that the exact time can be known to all

the network entities. The latter type is constituted by networks using variable length packets,

with a single clock for each different node. Intuitively, a synchronized network works better in

terms of throughput performances, but obtaining and maintaining data synchronization is not

a simple task in the optical domain: delaying schemes are required to fit the packets in fixed

time slots.

There were studies (Chin et al., 2000) on the node structures to allow synchronization

at the network edge, and to preserve it into the network core. The two regions are divided

because at the network edge the packets can arrive at each instant, and the aligning method

can be able to provide delays up to the packet length. On the other hand in the network

core the synchronization stage provides only little variations, to compensate the local jitters

induced in the transmission mechanisms. In fact the situation is slightly more complicated in

the optical domain because the time shifting of the packets is stressed by some of the non–

linear effects presented before. If the wavelength domain is used to solve contentions then the

27

chromatic dispersion, typically in the range of 20 ps/nm/km, needs to be taken into account

also; moreover the refractive index of the optical medium is a function of the temperature:

this delay variation on a long link can affect heavily the performances in the arriving node.

From this assertion is clear that the packets need to be synchronized in the network core also:

designing a network with links length multiple of a slot time duration is not sufficient to prevent

jitters between different nodes.

There are some well known main methods to align packets. In the first scenario — the

so–called logarithmic delay lines solution, see Figure 3 — many groups of different length fiber

delay lines, organized in a cascaded scheme, are used to obtain, summing all the different

delays induced by each stage. The precision of the synchronizer depends from the number of

stages; this number can’t exceed a threshold for the noise problems introduced by the FDLs.

A controller must choose the stages that the packet needs to pass in order to achieve the

desired delay. A second solution is called base–m AWG scheme (the architecture can be seen

in Figure 4): the Arrayed Waveguide Grating (AWG) — exploiting the interference properties

of arrayed waveguides — provides a fixed routing of an optical signal; the output port of

the specific signal depends from its wavelength: for this reason the routing can be driven

by Tunable Wavelength Converters (TWCs) before each input port of the AWG. If FDLs of

different length are connected in feedback configuration at each port of the AWG, by choosing

the right wavelength the packet can be delayed of the desired time. The resolution of the method

depends from the number of available FDLs, so from the number of input–output ports of the

AWG. This latter method is compact to be implemented in hardware, and it can be cascaded

28

Figure 3. An example of three stages logarithmic delay lines structure.

also to provide more functionality (see Figure 5). From the point of view of the performances

it introduces lower power penalties in comparison with the logarithmic delay lines, and it does

not suffer from crosstalk. Therefore future synchronization stages will be built looking at this

kind of architecture.

There is also another approach: using a piece of highly dispersive fiber and a tunable

wavelength converter: the transmission time of the packet in the fiber is proportional to the

wavelength, and so different delays can be achieved. This method however provides finite

resolution in the possible obtainable delays, due to the intrinsic characteristics of the tunable

wavelength converter.

A further distinction in the category of slotted networks can be done analyzing the packet

header position inside the time slot. In the first case the header can be put at the beginning of

the time slot: in this case the node is able to understand the beginning of each packet without

problems; on the other hand there is the necessity to provide each output interface with a fast

29

1

n-1

n

AWG

IN OUT

Figure 4. Architecture of base–m AWG scheme.

30

1

n-1

n

AWG

IN 1

n-1

n

AWG

OUT

Figure 5. Cascaded base–m AWG.

and high–resolution synchronizer to recover from the little jitters inside the node architecture.

These synchronizers are not needed if there is a guard time between the beginning of the time

slot and the header itself (second case): the drawback of this scheme is that the node has to

implement a fast recovery of the header on a packet–by–packet basis.

CHAPTER 2

ENABLING TECHNOLOGIES

In this chapter the new optical technologies that are currently object of interest for the

scientific community are described, stressing the changes and the challenges that they can have

on the today networks when will be commercialized.

2.1 Optical switch design

All the demands of the future information age — such as dynamic delivery of communication

services, improved scalability and flexibility — must be fulfilled by the new generation networks.

Therefore the designed network components have to support a particular architecture, which is

better suited for the dynamic global distribution of multi–media based services. One of the most

important tasks that are being studied today is the design of switches capable to perform all the

basic operations on the data in the optical domain: this will introduce the optical revolution

not only on the physical layer — as in the first generation optical networks previously described

— but in the routing operations also (Deng et al., 2000).

Optical core switches are studied to accomplish many different tasks, such as simple data

transport, high bandwidth cross–connect switching, or multicast communications, because a

wide range of data types are exchanged across a core network today. Medical imaging, voice and

video, interactive customer support and additional applications (that have yet to be discovered)

are only a partial list of all the applications that need these new services to be implemented.

31

32

But this is only a part of the new requests to the future networks: economical studies have

stressed the importance of the efficiency and manageability aspects, to lower the operational

costs. Moreover the way of construction of the new optical switches will affect heavily the

quantity of customers that can operate in a useful way in the network, and consequently the

economical advantages that the carriers can obtain managing the network. Up to today, two

major ways are studied: devices that perform opto–electronic conversion of the processed data,

and on the other hand all–optical switches, both of them promising great cost savings.

All–optical devices are seen as a integral parts of an all–optical network, in which information

is transported, managed and switched totally in the optical layer. This would provide high

bandwidth, small user–to–user delay, flexibility and scalability. But this kind of devices are not

yet ready, for the lack of technology, so the way to take the biggest advantage from the optical

domain today is to develop together all–optical and opto–electronic devices.

2.1.1 All–optical switches

If there is the possibility to manage and to switch light signals without converting them

into the electronic domain, it is possible to obtain an all–optical device. At the today state of

the art, an appealing way to build a scalable optical switch is represented by MEMs (Micro

Electro Mechanical Systems) technology: this solution is today the best chance to obtain a

switch matrix sufficiently fast and scalable in size to carry out the work of an important global

communication node; in fact many ports, each having fibers with hundreds of channels of

different wavelengths, represent a challenge for any existent electronic switching fabric today.

In this way all the advantages of the WDM technology can be exploited, in conjunction with

33

a great manageability. The possibility to pass the data transparently (without any conversion)

through the switch is appreciable in the situations where almost the total part of the traffic is

not directed locally, but has to be spread to all the neighbor nodes.

The MEMs technology can turn a beam of light by mean of special mirrors that can be

electronically controlled in all the three dimensional directions. The variety of the possible

movements that can be implemented allows to direct the light to a high number of ports — in

the order of thousands — in a single switch stage; thus minimal insertion losses are experienced

by the light, and this allow to obtain high performances. Recent studies (Robinson, 2001) have

shown that bidirectional devices with 4000 input/output ports are currently object of study.

It is clear that if the number of ports can be risen without much effort, then the obtained

device results highly scalable. Another aspect of scalability is represented by the possibility

of throughput variation, and it is also provided by the intrinsic switching method in MEMs:

a mirror is of course bit–rate and protocol insensitive. Combining all these characteristics the

vision of an all–optical switch is complete, and it can be seen as the perfectly scalable optical

device; a network built with such devices has an high degree of flexibility also: a user can

transmit the data in the format that is adherent to the service that must be implemented,

without worrying about restrictions of the leased line.

The way of all–optical devices counts also some points in which the limitations of this kind of

technology come out (Ramaswami and Sivarajan, 1997). A possible mistake is to think that an

all–optical component does not introduce any loss in the data: this is not true. Optical couplers,

for example, experience the ’return loss’ and the ’insertion loss’ phenomena: a small amount of

34

the entering signal is reflected back, and another part is lost inside the component; the level of

total loss is around 40 dB below the input power. For this reason an optical switching fabric

can loose up to 15 dB of the input power; this loss depends by many factors: the technology

used to build the single devices, the size of the fabric, and the number of stages counted by

the architecture. Therefore the number of stages is kept at the minimum to reduce the losses:

MEMs is a promising way for this aspect, because it permits to have an high number of ports

in a single stage. The minimum achievable loss in a single stage switching fabric is around 6

dB: this aspect has to be kept into account to avoid a too weak signal inside the fabric itself;

in this way the transmitting lasers have to receive sufficient power, so increasing the cost of the

network equipment.

2.1.2 Electronic based switches

The second type of optical switches that are being studied today bases the fabric on elec-

tronics: this allow to perform easily the current functions of network management — such as

fault location and performance monitoring —, however maintaining high bandwidth services.

Of course this type of switches reuses some of the common standards (as SONET), and this

makes easy their integration in the existent networks (Listanti et al., 2000); moreover some new

standards are studied to improve the possible types of communications, while reducing pro-

visioning times for these types of switches: for example GMPLS (Generalized Multi–Protocol

Label Switching) is a variation of the MPLS (Multi Protocol Label Switching) in the way of

optical dynamic networks (Murata and Kitayama, 2001).

35

Electronic–based optical switches are currently substituting the more common electronic

switches, and this helps to keep low the cost of an high bandwidth dynamic network (Xu et

al., 2001). However this fact must not be seen as the will to avoid optics, but instead a way to

approach a radical change: the optical revolution is introduced slowly in the electronic networks,

limiting risks and avoiding non predictable unwanted behaviors.

2.1.3 Space and power

Another important aspect that has to be taken into account designing a switch is the

occupied space and absorbed power by a working fabric. Many fibers can be put in a single

cable, and with WDM technology many channels are carried on a single fiber: this implies a

big communication equipment, capable to switch enormous amount of data. Opto–electronic

conversion of the signals requires electronic chips that have to be powered, and of course this

means space occupation. The all–optical equipment represents an advantage from this point

of view: a cross connect operating in the optical domain with one thousand of input/output

ports reduces of one order of magnitude the occupied space with respect to an analog electronic

device designed for the SONET hierarchy. Moreover the absorbed power is thirty times bigger

in the electronic case. For each device the indexes that indicate the space and power saved —

with respect to the electronic case — varies, but for an optical switch the floor space saving can

be 92% and the power can be reduced of the 96%. These changes have as directed consequence

a big reduction in the network maintenance costs: investments in batteries, generators and

building rents are considerable high in the modern data exchange centers.

36

Having in mind all these considerations about optical switches, it is possible to look inside

some projects, aimed to build devices operating in the optical domain, for the next generation

optical networks.

2.1.4 Keys to Optical Packet Switching project

One of these projects is the Advanced Communications Technology and Services (ACTS)

KEys to Optical Packet Switching (KEOPS) one (Guillemot et al., 1998; Gambini et al., 1998),

a wide topic study with the aim of transfer almost all the data operations from the electronic to

the optical domain, to obtain an all–optical switching equipment. In this contest the network is

considered slotted, with packets of equal length and headers encoded at low fixed rate to allow

data processing using the common electronic devices. For what regards the node architecture

it proposes a scheme to transparently implement optical packet switching: the blocks that

forms the entire node structure are the input block — composed by a coarse synchronizer,

implementing jitters elimination and headers recovery — a switching fabric that directs the

payloads to the designed output port, and a third output interface, with the task of regenerating

the signals, and header updating.

The switching fabric can be implemented in two ways. The first scheme (see Figure 6 and

appendix A for a list of the optical components) solves contentions using a set of delay lines

for each input port in forward configuration; each input has access to, at least, one FDL to

guarantee enough flexibility; among all the available FDLs, the shortest one is chosen. After

being delayed, the packets are selected by filters, they are sent to the right output port, and using

interferometric wavelength converters the signal is regenerated. The second way is constituted

37

1

n n

1

Figure 6. The first scheme for the switching fabric proposed by the KEOPS project.

38

by a broadcast–and–select space: a scheme is depicted in Figure 7. In this model each input is

converted to the right wavelength, then all the signals are multiplexed and sent to all the delay

lines, so it is possible to have a copy of the inputs for each possible delay; then for each output

port the signals with the right delay are selected using Clamped–Gain Semiconductor Optical

Amplifiers (CG–SOAs); the final stage demultiplexes the signals and chooses the right signal

among all the wavelengths. This type of switch has the interesting characteristic to support

multicast: by tuning properly the SOA gates it is possible to send the same signal to all the

output ports. However the main drawback of this configuration is the high quantity of used

components, that increase the cost of each switching unit: the number of wavelength converters

and multiplexers/demultiplexers grows linearly with the number of ports, while the required

SOAs are proportional to the square of this number.

2.1.5 Wavelength Switched Packet Network project

In the WAvelength Switched Packet NETwork (WASPNET) project not only node design

and routing issues are considered, but the aspects related to transmission schemes of the packets

and device fabrication also (Hunter et al., 1999).

The switch architecture studied in this project is powerful and easy to be implemented

(see Figure 8); the fiber delay lines are used in feedback configuration: they are additional

output ports of the switching fabric and terminates as input ports; in this way the FDLs are

used as exceptional ports to solve contentions on the output ports. Each FDL is composed by

its own Tunable Wavelength Converter (TWC), a set of parallel fibers of different length, and

a second TWC: the particular fiber that has to be crossed by a packet in the FDL depends

39

1

n

11

1

k

n

k

n

1

k

Figure 7. The second scheme for the switching fabric proposed by the KEOPS project.

40

1 1

11

n-1

n

n-1

n

n-1 n-1

n n

2n 2n

n+1 n+1

2n-1 2n-1

AWG

AWG

Figure 8. The WASPNET switch architecture.

41

from the particular packet wavelength; consequently the total delay experienced by a packet

depends from its wavelength. When a packet enters in the FDL the TWC converts it in the

proper wavelength to avoid contentions at the output ports, then it is re–converted at the end

of the FDL by the second TWC; then it enters again in the central switching fabric, constituted

by an AWG.

The described architecture can switch the traffic of one particular wavelength for all the

incoming fibers: in the case of multiple channels (WDM), equal planes (working in parallel)

must be provided for each wavelength.

Operating at 2.5 Gbps and 10−10 for the Bit Error Rate (BER) the proposed switch has

registered a power penalty of 2.5 dB — mainly due to the insertion loss in the AWG — in a

configuration with 32 input ports.

2.1.6 Shared–Memory Optical Packet switch project

The Shared–Memory Optical Packet (SMOP) switch (Karol, 1993) can be seen as a simpler

implementation of the WASPNET one (see Figure 9). In this case a space switch has some input

and output ports dedicated to the fiber delay lines, as before. The difference lies in the fiber

loops implementation: in the WASPNET architecture each FDL can offer a range of delays,

chosen by the TWCs. Here each FDL is a simple piece of fiber with a certain length, so the

delay that can be experienced by a packet — when the FDL is chosen — is fixed. The length

of the delay lines can vary from one to many packet durations. The longer delay lines help to

keep low the number of recirculations of each packet, contributing to not introduce noise from

42

1 1

2 2

m m

n n

1 1

n-1 n-1

IN OUT

Figure 9. The SMOP optical switch.

43

amplification; however the number of recirculations a packet can suffer is unpredictable, so an

amplification stage at the output is necessary.

2.1.7 Asynchronous Transfer Mode Optical Switching project

A single stage feedback switch equipped with optical buffering was developed in the Research

and Development in Advance Communications in Europe (RACE) Asynchronous Transfer Mode

Optical Switching (ATMOS) project (Masetti et al., 1996). This switch operates with a different

philosophy with respect to the other ones: the core is represented by a two input–two output

ports coupler (Liu et al., 2001), one input and one output ports of which are reserved for the

fiber loop (see Figure 10). This loop can carry many wavelengths, and for each of them there

is one SOA gate.

The signals are multiplexed on a fiber and then half the power goes to the loop, while the

remaining power goes through the coupler to the output ports, provided of a tunable filter to

select the correct signal. If contention occurs, one signal is sent to the output, while the other

one is maintained in the recirculating loop by turning on the respective SOA gate. This type

of architecture can implement class differentiation (Ribeiro and O’Mahony, 2000), because the

packets that are waiting in the loop can be preempted by new packets of higher classes.

2.2 3R all–optical regeneration

Designing a scalable all–optical network, a key issue that has a relevant importance is the

signal quality, represented by the signal to noise ratio, shape of the signal and synchronization of

it. Through the way to its destination each chunk of data has to pass an unpredictable number

of nodes, it can be kept recirculating many times in FDLs, the length of the links can vary

44

EDFA

Figure 10. The ATMOS switch with fiber loop memory.

45

from one path to another: non–linear transmission effects (such as crosstalk and distortions)

and noise accumulation can represent a big problem for any paths in the network. For these

reasons each node structure has to provide solutions to keep the signal quality high, in order

to have a usable signal.

Therefore researches has begun to investigate the possibility to regenerate an optical signal

without electronic conversion (Sartorius, 2001). A complete regeneration of the signal is com-

monly indicated as 3R (Re–amplification, Re–shaping and Re–timing). To amplify a signal in

the optical domain (1R regeneration) is easy: erbium–doped fiber amplifiers and semiconductor

optical amplifiers have improved their performance in the last years (see section 1.3) and it is

now possible to amplify many WDM channels simultaneously, independently from bit–rate and

data format; however this method does not solve the problems related to non–linear effects,

like crosstalk, and some noise is added to the signal. To eliminate these undesirable effects a

2R regeneration is needed: to shape a signal it is possible to use a non–linear gate in which a

probe signal is inserted; the data signal drives the gate. In this way the upper bound for the

bit–rate is represented by the speed of the gate, and each WDM channel has to be threaded on

its own.

The most complete regeneration for a signal implies re–timing also. The 3R regeneration can

be accomplished in a similar way to the 2R one: the only difference is that the non–linear gate

is probed by the clock signal. Therefore the clock signal defines the shape of the output also,

and in this way it is responsible not only for the re–timing part of the process: for this reason

46

3R optical regenerators can’t be independent from data rate and format, and this limitation

can reduce severely the scalability and flexibility of an optical network.

In 3R regeneration the clock signal has to be synchronized with the data stream. This

alignment can be done in the optical domain using mode–locked lasers and self–pulsating DFB

lasers; today experiments have successfully shown the efficiency of this method up to 40 GHz.

Non–linear gates can be obtained using Mach–Zehnder interferometers with SOAs embedded

in each arm to control transmission: this solution has been shown to work up to 100 Gbps.

2.3 Fiber delay lines design

As seen before in section 1.2.3, fiber delay lines (FDLs) are the only viable way to implement

optical buffers today. Their design is easy because they are simply pieces of fibers aimed to

be crossed by the packets that must be delayed. Problems on FDLs dimensioning do not arise

in almost all the forward configurations, as the first KEOPS solution: in this case FDLs are

crossed only one time from the data in a single switch, and they give exactly the wanted delay.

On the other hand some attention has to be paid to the feedback configurations, where FDLs

are designed to work like a buffer.

One of the differences with respect to a real buffer is that they introduce noise to the signal,

and it isn’t possible to store data indefinitely; this problem however can be overcome with

optical regeneration of the signal. The other main question is that a single fiber has a finite

storage time: after a certain period of time the packet arrives at the end of the FDL, and it

must be re–routed (supposing a feedback configuration). The fixed traveling time of a packet

in a FDL con be a problem when the network is unslotted, and no constraints are put on the

47

packet length: the simplest solution is to choose fibers that can contain the longest packet,

but this implies that the time resolution of the buffer is very small. In (Callegati, 2000b) an

analytical model (and simulations also) are performed showing that the packet loss probability

strongly depends from the fiber length, and it can differ for three or four order of magnitude

in different cases. Given a set of B fibers for each node, each giving a different delay — from

D packet length to (B − 1)D, where D is the average packet length — the optimum length of

the fibers results to be around 0.25 of the average packet length: adopting this criterion the

best performances can be obtained by the unslotted networks. The optimum value of the fiber

length is also shown to slightly depend from the value of B.

Up to today the only way to obtain a FDL with variable storage time is to use more FDLs

of different lengths, as in the WASPNET project and the ATMOS one (described in sections

2.1.5 and 2.1.7). Another innovative solution is shown in a recent study (Sakamoto et al., 2001):

each FDL is provided of many fixed wavelength converters, that converts from λm to λm−1,

with m = 1, . . . ,M (see Figure 11). The output of the FDL is sent to a fibre Bragg grating

(FBG), to filter out λ0, that exits from the loop. In this configuration, when a packet is inserted

at the wavelength λm, it recirculates in the loop until it is converted to λ0, and then exits. So

the obtained FDL provides a delay time dependent from the entering wavelength of the packet.

Each converter is preceded by a SOA, to suppress signal degradation. The major drawback of

this method is the intrinsic complexity of a single FDL, requiring much more components than

a simple piece of fiber.

48

OUT

IN

Figure 11. Scheme of FDL designed to obtain variable delays.

2.4 Optical Burst Switching

Optical Burst Switching (OBS) starts from the observation that it is impossible for a node

to elaborate a header in the small time needed to receive a packet, taking into account the

enormous bandwidth available on optical fibers (Turner, 1999). This problem can be solved

buffering the packet while its header is processed, but the difficulties in storing packets without

any opto–electronic conversion make this way not the optimal solution. OBS is a proposed

solution to overcome this problem.

The approach divides the entire network in two regions: edge and core. At the edge the

usual packets are assembled with some procedures to form a burst: a collection of packets

that have in common some features — like destination, or quality of service requirements —.

For each single burst a new Control Header Packet (CHP) collecting these important data is

49

created and it is sent on a separate channel with respect to the burst it is referred to (Wei and

Jr., 2000; Widjaia, 1995). The burst must be sufficiently long to allow the node receiving the

header to convert it into the electronic domain, to elaborate and to update it (if necessary),

and to prepare the switching fabric of the node for the arrive of the burst that transparently

passes through the node. The burst header can be sent to the next hop before the burst itself

to give the node enough time for all the operations: in this case it works as a reservation of the

resources based on a hop–by–hop scheme (de Miguel et al., 2001). The bursts travel only in the

core nodes, and when they arrive at the network edge they are disassembled into the original

packets and delivered with the usual methods.

2.4.1 Burst generation

The way of assembling packets to form bursts can affect heavily the network performance: in

fact the characteristics of the network traffic depend from this assembling method. Studies (Ge

et al., 2000) have shown that the Internet traffic has intrinsic fractal characteristics, and this

fact can have important impacts on the buffers of the network nodes, degrading the network

throughput (Callegati, 2000a). The degree of self–similarity of the traffic can be taken to

indicate how a traffic is suitable to be managed by the network: the lower this coefficient, the

higher the random characteristics of the traffic, the better the throughput.

The way a burst is formed has to take into account other aspects also. First of all, a burst

has to be bigger than a minimum size; it is determined by the capacity of the control channel: if

no new CHPs can be put on the control channel, no new bursts can be put on the network. On

the other hand if there are few data arriving at a boundary node, making a burst of sufficient

50

size means to introduce a huge delay for the first arrived packets, and to waste the network

bandwidth for some time. Thus burst assembling has to be studied with attention. A research

on this argument (Ge et al., 2000) has shown that a simple burst assembling algorithm can

reduce the degree of self–similarity of the traffic, with respect to the previous constraints. The

proposed algorithm follows some simple rules:

• the packets are queued separately with respect to the their destination;

• when the first packet is put in a queue the timer for that particular queue is started;

• all the packets of a queue are sent on the data channel — forming a burst — when the

timer reaches a threshold, the respective CHP is sent on the control channel and the timer

of the queue is reset;

• if the burst is not sufficiently long at the time threshold, it is padded to reach the minimum

length.

This method satisfies the minimum size requirement for the bursts, and simultaneously poses

an upper bound on the delay suffered by the packets in the assembling process; simulation proves

that it can reduce the Hurst parameter of the traffic offered to the network, that means low self–

similarity degree. The reduction of this parameter is proportional to the time threshold used

to assembly packets: higher the threshold, lower the Hurst parameter. Of course this threshold

can’t exceed some values, to not increase too much the packet delay: different applications can

suffer different delays, so there isn’t a unique way to indicate an upper bound for this quantity.

51

2.4.2 Channel scheduling

Operating with OBS a new aspect in switching functions must take the attention of the

designer: the channel scheduling, in other words the way to choose the output channel —

among all the suitable ones — for a given burst (Turner, 2000; Ogushi et al., 2001; Verma et

al., 2000). A channel is a simple unidirectional link between two core nodes, and it can be

physically represented by a wavelength in WDM technology, or a part of it in code–division

multiplexing. A burst occupies the channel for a long period of time, and it has to pass

transparently through the switch. Therefore the switch control unit — analyzing the CHP

received on the control channel — must reserve the right channel in advance; the time of the

burst arrive and its length are derived from the CHP also. The controller maintains information

about the already scheduled reservations, to be able to select a free one at the right moment.

The selection of the channel can be performed in two ways (Xiong et al., 2000). The first

and simpler way makes use of the LAUC (Latest Available Unscheduled Channel) algorithm.

In this case the controller memorizes for each data channel the time when the latest scheduled

burst finishes (channel future available time): from that instant the channel is free. It tries to

minimize the gap between the end of the latest burst and the beginning of the unscheduled one;

an example of channel scheduling is shown in Figure 12: in case (a) channel C2 is selected; in

case (b) one recirculation in a FDL is needed, and then channel C3 is chosen. When a control

packet arrives the controller analyzes the CHP and finds out the set of data channels that can

be used to forward the burst, the burst arrival time t0 and its length L; then it selects the

channels that are not scheduled yet at time t0. Among all these channels it chooses the one

52

t

t

t

C1

C2

C3

t 0

t

t

t

C1

C2

C3

t 0 t + D0

(a)

(b)New burst

New burst

Figure 12. An example of LAUC scheduling.

53

that minimizes the gap between the end of the latest burst and beginning of the new burst. If

the operation has success — this implies that there is at least one unscheduled channel at time

t0 — the future available time for the chosen channel is updated to (t0 + L). There could be

the possibility that all the channels are scheduled at time t0: in this case the controller repeats

the control of the data channels at time (t0 + mD), where D is the delay given by a FDL; m

can range from one to the maximum number of consecutive recirculations in a FDL. This upper

limit is determined by the components of the switch, and from the noise added to the signal for

each recirculation (Kushwaha et al., 2001); if the FDLs are provided with regenerators this limit

can be virtually infinite. The advantage of this algorithm is its simplicity, that is an important

aspect in a high speed environment. The cost of this simplicity is paid from the efficiency point

of view: the gaps between consecutive bursts on the same channel are never utilized.

A variation of this algorithm represents a more efficient way to select channels: the LAUC–

VF (Latest Available Unused Channel with Void Filling) algorithm, as its name suggests, tries

to fill the voids between consecutive bursts to exploit more efficiently the available bandwidth.

Given the length L and arrival time t0 of the burst it searches a free channel in the period

of time (t0, t0 + L), and among all the available data channels it chooses the one with the

shortest gap between t0 and the end of the latest burst before t0. An illustrative example

is given in Figure 13: in this case the new burst is scheduled in channel C2. If no channels

are free in this period of time it performs another search for eligible channels in the period

(t0 + mD, t0 + mD + L). When a suitable channel is found, the information on it is updated.

It is simple to see that this algorithm exploits very well the bandwidth, obtaining a good

54

t

t

t

C1

C2

C3

t 0

t

t

C4

C5

New burst

Figure 13. An example of LAUC–VF scheduling.

scheduling for the outgoing channels; the main drawback is represented by the complexity: if

the controller is not able to prepare the switching fabric in time for the burst arrival (because

the search of the optimum channel requires too much time), all the data would be lost. This

search has to be finished in an acceptable time, and this increases the cost of the hardware

implementation.

2.4.3 Quality of Service support using Optical Burst Switching

The future applications will ask an high degree of flexibility to the network: the services

provided by the network will be very different, and there will be the necessity to be able to

satisfy a large amount of requests with different characteristics (Jeong et al., 2000). One of

55

the possible features that can be introduced in the Internet traffic is the differentiation of

class services: this implies that packets (or bursts) belonging to different classes would receive

different services by the network switches.

OBS can be designed to provide quality of service in a very efficient way: the burst assembly

mechanism described in section 2.4.2 can be modified to assembly packets that have in common

not only the destination, but the type of service that they need also. Of course to distinguish the

service that a node must provide to a certain burst it is possible to include some information on

the CHP (Control Header Packet) sent on the control channel: in this case the node — analyzing

the CHP content — would choose the data channel in a proper way; for example it would be

possible to delay an already scheduled burst if another burst with higher priority arrives at the

node. This way to differentiate among bursts has many drawbacks: first of all, more data must

be embedded in the CHP, and consequently they must be analyzed by the switch control unit

(at least the information on the burst class); in addition, it requires more complexity in the

algorithm that schedules the channels, resulting in a longer scheduling time and more expensive

switch implementation. Another simple method to obtain class differentiation is to associate

each WDM channel with a class of service: this approach is very simple but results in a waste

of bandwidth if the quantity of the bursts of one class are below the expectations.

These drawbacks could be seen as the unavoidable costs that must be paid to introduce

classes in the network, but OBS can offer a more effective way to perform this differentiation, as

well explained in (Yoo and Dixit, 2001; Qiao, 2000; Yoo et al., 2000). The specific characteristic

of the OBS that can be successfully exploited to obtain different services is the offset time: the

56

time between the CHP and the arrival of the correspondent burst. OBS assumes that the CHP

is sent on the control channel before the burst it is referred to, allowing the switch to analyze it

and prepare the switching fabric. If a CHP is sent out with a bigger offset time, the algorithm

that schedules the channels can find a free channel with an higher probability: in fact it ’knows’

the time arrival of the burst much more in advance; therefore a burst having a bigger offset

time receives a better treatment. If only two classes are implemented, and supposing that to

the class number one is assigned an offset time equal to t0, it is possible to obtain a higher

level class — labeled as class number two — using a new offset time equal to (t0 + L): the

difference in treatment between the two classes is determined by L. If the time duration of the

longest packet of class one is less or equal to L, the bursts of class number two can never be

blocked. To be convinced of this fact it’s sufficient to analyze the worst case: when the CHP of

class one arrives just before the CHP of class two, and the two bursts have to be routed on the

same channel. Supposing that the header of class one burst is analyzed at time t, the switch

control unit will reserve the channel for this burst in the interval (t + t0, t + t0 + l), where l

is the time duration of the burst. Then the controller searches a channel for class two burst:

since the offset time is equal to (t0 + L) > (t0 + l), class two burst is expected to arrive at time

(t + t0 + L), when the channel (already scheduled for class one) is already free.

A class is said to be isolated when its bursts are never blocked by inferior class bursts: as

seen before, complete isolation of generic class i can be achieved assigning an offset time equal

the offset time of class (i− 1) plus the time duration of the longest burst of class (i− 1). If the

duration of the bursts is not upper limited a complete isolation is not possible: in this case the

57

class isolation degree — the fraction of bursts that are not blocked — increases with the offset

time, approaching to one when the offset time goes to infinity.

It can be observed that high priority means high offset time, and this implies a high delay

when the burst is going to be sent out after the assembling mechanism; this is in part true:

an already made burst — after its CHP is sent to the control channel — has to wait a period

equal to the offset time of its class. But this latency is experienced only in the passage between

the edge and the core of the network, and it is not high: considering the huge bandwidth given

by the optical fibers, when four classes are implemented with an isolation degree of 95%, the

resulting offset time for the upper class is 108 µs at 10 Gbps (Yoo and Dixit, 2001). This delay

can be easily suffered also from high sensitive applications such as voice and video transmission.

CHAPTER 3

SIMON: AN ALL–OPTICAL NETWORKS SIMULATOR

To engineer properly an all–optical network it is necessary a way to test the different solu-

tions that can be adopted. Since there isn’t the possibility today to have the components for

the all–optical treatment of the signals — and of course this way would be very expensive —,

a possible way to find out the true behavior of such a network is to simulate it.

This chapter explains how SIMON (SIMulator for Optical Networks) was obtained. It starts

with the description of the ATM simulator (CLASS) used as basic structure for the new one;

then the modifications performed on the CLASS software — aimed to obtain this new kind of

simulator operating on the new generation all-optical networks — are explained.

3.1 The starting simulator: CLASS

CLASS (ConnectionLess ATM Services Simulator) is a simulator written with the aim to

calculate the performances of ATM networks. It was developed by the Electronics Department

of Turin Polytechnic in co–operation with CSELT (Centro Studi E Laboratori Telecomuni-

cazioni), now TILabs, one of the most important Italian research centers for what regards

telecommunications and networking. The last version (namely 6.20f) had the contribution of

the Technical University of Budapest and of the Wurzburg University.

CLASS can simulate the behavior of an ATM network of general topology, with very great

variety of different elements: traffic sources, connections, links and node types. All these data

58

59

are provided to the program through a text file, written in a specific formal grammar called RC

language (this language is described in the following). The network is assumed to be slotted:

all the nodes are synchronized by a general master clock. The capacities of all the links must

be a fraction of a capacity defined at the beginning of the simulation; in addition, all the links

must be bidirectional and there isn’t the possibility to have a link that starts and terminates

on the same node. For a complete description of this software refer to (Tur, 1998).

The simulator is a computer program written in C language. The code is compatible with

many operating systems: it can be compiled on workstations and personal computers running

MS–DOS, Windows (from 95 to 2000 and the NT version), Linux, Solaris, and generally all the

Unix systems providing a C compiler.

3.1.1 Network description

In CLASS the network is composed by three main elements: the nodes, the links and the

users. Both the characteristics of these elements both the physical topology of the network must

be described by mean of a formal language called RC. The text file — written in RC language

— containing all the data of a specific network represents the input of the simulator; before

starting the simulation CLASS performs a first check on the syntax of this file, and then — if

the control is successful — it checks the logical validity of the described network. For example

if a link is not connected to any node, or if any of the input parameters are contradictory, then

the program stops giving out a specific RC error message. If these two checks are passed, the

network description is considered right and the simulation is started.

60

There are many elements in the network that can have the same characteristics: to specify all

of them for all the network entities could be very long. For this reason the RC language provides

an easy way to specify globally the value of a parameter. For example through the ’default’

statement the capacity of all the links of the network can be specified; these global declarations

have to be placed at the beginning of the RC file. The global value can be overridden in the

case a particular value is specified for a single network element: this guarantees the maximum

compactness of the network description, without loosing in clearness. This language provides

also the possibility to ask some parameters at run time: this can be useful to run different

simulations with the same description file.

Nodes, users, and links entities that can be managed by CLASS are described in the following

sections.

3.1.2 The node structure

Each node of the network has to be declared in the RC file. For each node the neighbors

are listed, within the respective links. The users that are attached to the node have also to be

indicated, each of them with the link going from the node to the user. The other parameters

that can be set for the nodes include the node elaboration delay — the time required by the

node to elaborate an information —, and the policing type adopted by the node in the case of

TCP connections.

Each node can be enabled to perform traffic policing for each input link; the algorithm

used to implement this function is the Virtual Scheduling Algorithm, described in the recom-

mendation I.371 of ITU. It is also possible to allow up to four different classes of service: the

61

dimensions of the buffers that would be reserved for each class at each link can be also specified.

Since the aspects of traffic policing and classes differentiation are not further implemented in

the modified version of CLASS, these aspects are not treated anymore in the following.

3.1.3 CLASS user types

As the name suggests, CLASS was born to simulate connectionless traffic; however the

developers of the project added many characteristic through the years, and the results are

represented by the latest version (6.20f) that can measure the performances of many types of

traffic, including connection oriented services.

The users are intended to be the traffic generators that introduce their cells into the network.

They must be attached to a node, and they can’t count more than two links: one to the node

and the other from the node. The users can be of different types, each of them distinguished

by the method the new cells are produced, or by their traffic shaping policy. Each user has an

output buffer in which all the already produced messages are put, waiting to be pushed into

the network.

Traffic shaping is the name given to all the controls that the users act on their own traffic

to verify that it is conform to the parameters negotiated with the network. The network also

performs some controls on the traffic generated by the users, and this operations are referred to

be traffic policing (described in the following section). A user implementing traffic shaping runs

an algorithm able to determine the time a new cell can be put on the network. The first type of

users performing traffic shaping in CLASS have a single shaper for all the produced traffic: this

shaper computes the time interval that must pass between two insertions of consecutive cells

62

into the network, regardless to the cells destinations; this method is called VP–based shaping

policy. The second traffic shaping method — namely VC–based traffic shaping — needs one

shaper for each virtual circuit maintained by the user; after the shaper, the different cells are

multiplexed to be put on the transmission queue. There are two methods to multiplex the cells

in CLASS: one is a standard FIFO multiplexing, while the other one minimizes the delay jitter

in the cell flow.

The connectionless traffic generators can be divided into four different groups. The first type

— referred as type one for sake of brevity in the following — produces messages with constant

length (that can be specified in the RC file). The arrival times of the produced messages form a

Poisson process; no traffic shaping is performed by this type of user. The second connectionless

generator (type two user) has all the characteristics of the first one, with the exception that

messages of two different lengths are produced — bimodal distribution type —, each one with

a certain probability; all these parameters can be specified in the RC file. The third type

(number three) can be seen as a larger generalization of the first two types: in fact in this case

the length of the messages are distributed according to a truncated geometrical distribution;

the maximum message length is fixed to 200 cells, and the average cell length can be indicated

in the RC file. The last type of connectionless user provides a traffic generator that follows the

output of a DQDB MAN; since this type of user is no longer supported by the modified version

of CLASS, its description is beyond the scope of this section.

For the connectionless user types it is possible to specify the total traffic that these entities

would offer to the network; the fraction of traffic that each user must produce can be indicated

63

in a traffic relation file. This file contains also how the traffic of a connectionless user is divided

among all the other ones: in this way the relations between all the couple of users can be easily

specified. If the traffic relation file is absent, an equal distribution is supposed: the total traffic

is divided equally among the total number of connectionless users, and then the fraction of

traffic for each single user is furthermore divided among all the other ones. In this last case the

traffic is spread equally among all the couplers of connectionless users.

Other connectionless traffic generators can be designed starting from the previously de-

scribed user types, and considering the two different traffic shaping policies: in this way new

eight types can be obtained.

As previously stated, CLASS provides connection oriented traffic generators also. In this

case it is mandatory to specify the other connection oriented user the cell stream has to be

directed to. Another difference with respect to the connectionless type is that the rate at which

the traffic is produced has to be specified individually for each user. There are two main types

belonging to this category: the first one (type four user) produces cells with a constant bit–rate;

if this rate is a non integer value the traffic generator adjusts the instantaneous speed of cell

generation to oscillate near the desired value. For the second type of connection oriented user

(type five user) an ON and OFF periods can be distinguished: in the ON period it generates

messages with constant bit–rate in a way similar to type four; in the OFF periods it does not

produce any message. The ON and OFF periods follow a geometrical distribution, and the

mean value for each period can be specified in the RC file.

64

3.1.4 Network links

The links are intended to be the physical media that carry the data through the network.

They can connect nodes to users or vice versa, or two different nodes; it is not possible to

connect two users directly. A link is supposed to be unidirectional, with a specific source and

destination; a CLASS restriction is represented by the fact that for each link going in one

direction there must be another link going in the opposite direction: this is reasonable, because

in almost all the cases of interest the links are always bidirectional.

The link parameters that can be set are the buffer for each different class of service, the link

length in km, the capacity of the link, and some other parameters to take information about

congestion and cell policing.

The link capacity is assigned by specifying a number called the ’timescale’. This value

indicates how many times the biggest capacity of the network (called the ’reference’ capacity)

must be divided to obtain the current link capacity. If a link has timescale equal to four, and the

reference capacity is 1 Gbps, then the link will have a capacity equal to 250 Mbps. This method

is required by the fact that the network is supposed to be unslotted, so the shortest period of

time that can be managed by the simulator is the cell time duration on the link with the biggest

capacity. This implies that all the capacities of the links in the network must be expressed as

an integer sub–multiple of the biggest capacity. This could be seen as a severe limitation in

the potentiality of the simulation, but this in not true: in fact there is the possibility that the

reference capacity would not be used in the network. For example, if a network supports links

65

at 200 Mbps and 300 Mbps, the reference capacity can be set to 600 Mbps, and the timescale

to three and two for the different links, respectively.

3.1.5 Routing implementation

For any virtual circuit present in the network CLASS assigns a path from the generator

to the destination. The implemented algorithm works as follows: it searches all the possible

routes for each traffic relation (represented by a unique virtual circuit); then chooses the path

that corresponds to the links that are less loaded at the moment of the route assignment. If

there are multiple paths with the same links load, it considers the number of crossed nodes, and

then the length of all the links crossed by the relation. If there are already some paths with

the same characteristics after all these considerations, the first encountered one is accepted for

the specific relation.

A good aspect of this method is that it is not a fixed shortest path assignment: the cells are

routed into the network trying to obtain an equal distribution of the load on the different links.

If for some reason it is desirable to force a particular route for a source–destination pair it is

possible to specify it in the RC file: in this case the default route is overridden by the imposed

one. It is possible to force a specific route between two nodes: in this case all the connections

from the users of the first node to the users of the second node will follow the assigned path.

Another possibility is to fix a node–user path: all the users of the source node will route their

cells to the destination user through the specified path. In addition it is possible to fix routes

between user–nodes, and between two users.

66

3.1.6 Simulation accuracy

CLASS provides a clever way to determine when to stop the simulation: it uses a module

that collects statistical information about some parameters. When the mean value of the

samples of the significant statistical parameter are within an accuracy interval with probability

equal of higher to the confidence level, the simulation is stopped. The default value for the

accuracy is 2%, while the default confidence level is 95%. With these two parameters it is easy

to obtain simulations with the desired level of accuracy.

The performance indexes that can be observed by the statistical module are five, only one

of them can be chosen to be the significant one (the parameter that drives the duration of the

simulation):

• the user–to–user cell delay;

• the network delay (the delay suffered from the cell within the network);

• the user–to–user delay experienced by the first cells of the messages;

• similarly the forth index represents the user–to–user delay of the last cells of the messages;

this parameter can be seen as the message delay also, because a message is passed to the

higher level protocols only when all its cells are reassembled, therefore the delay of the

last cell of a message is the delay suffered by the entire message also;

• the fifth parameter that can be observed is the cell loss rate in the network.

67

3.2 CLASS modifications

All the modifications performed on the CLASS code are aimed to modify the type of the

simulated network, and to obtain SIMON: a software capable to work on the new generation

all–optical networks.

As in the case of CLASS, its code is thought to be complete and easy to be understood,

portable and optimized from the computational point of view.

3.2.1 Network description

SIMON uses the same RC language developed for CLASS. This allows to quickly describe

many different network topologies in a particularly simple way; therefore the obtained simulator

is flexible and immediate to use. For an example of RC file see appendix B.

The only difference that must be noticed is that the links indicated in the RC file are

intended by SIMON as single wavelength channels. Optical networks using WDM have the

particular characteristic to use many wavelength channels embedded on a single link: these

different channels must be indicated in the RC file. This allows to collect information on the

different channels that form a link, and not for the entire link only. The links that go from a

node to a user and vice versa are limited to a single wavelength: it is reasonable since these links

can be seen as the access to the network core, and many network architectures are developing

in this way (Yao et al., 2001a).

It can be observed that specifying each channel for each link can produce a very long RC

file for the whole network description; this is true since WDM can enable the use of dozens of

wavelengths on a single link, and much more in the future. To make the description of a large

68

network easier an add–on utility is embedded with SIMON: it reads an RC file as input and adds

a fixed number of wavelengths to each link of the network. The number of added wavelengths

is a free parameter that can be specified calling the program. In this way the SIMON user can

decide to write the main topology, declaring only one channel for each physical link; then using

the embedded utility the complete description can be obtained immediately. This program

is topology independent, and it can also add channels to networks with a variable number of

wavelengths for each link yet.

The capacity to manage users is an important aspect in the optical network analysis because

it permits to study new techniques of packet switching, for example Optical Burst Switching

(OBS) described in section 2.4. In this way the users can be seen as the bursts assemblers —

implementing the functions described in section 2.4.1 —, and the rest of the network as the

optical core (Jourdan et al., 2001; O’Mahony et al., 2001; Mukherjee et al., 1996). It is easy to

understand that SIMON represents a flexible tool, capable to be adapted to different situations

with minor changes.

3.2.2 Packets generation

CLASS users generate messages according to the specific traffic generator types, explained in

section 3.1.3. Then these messages are divided into cells of fixed length (53 bytes), following the

rules of the ATM protocols. SIMON is designed to simulate packet–switched optical networks,

so some rules have to be changed. First of all SIMON users don’t produce messages but packets

of fixed length: this length can be varied easily into the code, allowing to fit the simulator to

69

different situations. In addition, the users don’t perform any traffic shaping; this implies that

some CLASS user types are no longer supported.

The available traffic generators are limited to three: the first type generates traffic with time

arrivals that follow a Poisson distribution; it is the correspondent of CLASS type one users,

forcing the message length to a single cell: for this reason this kind of user is called type one

in the following. The second type produces packets at constant bit–rate: it is implemented as

the CLASS type four generator, so it is called type four also for sake of simplicity. The last

available type corresponds to CLASS type five: it produces packets at constant bit–rate in the

ON periods, no packets in the OFF periods; ON and OFF periods are exponentially distributed

with mean value specified in the RC file.

3.2.3 Node architecture

Many different architectures have been proposed and studied in the past, the most important

ones are described in section 2.1. SIMON adopts a single stage feedback scheme similar to the

SMOP one (see section 2.1.6). The node is formed by three stages: the first one includes

demultiplexers and header recognition systems; the second one is the switching fabric, and the

last one is made by regeneration components and multiplexers. All these parts are controlled

and driven by the switch control unit. A scheme is reported in Figure 14 (for a list of all the

used components, see appendix A).

The first stage demultiplexes the input signals, extracts the packet headers and sends them

to the switch control unit that analyzes the information to prepare the switching fabric. Since

these operations require a fixed period of time to be performed, the payloads are delayed with

70

1 1

2 2

m m

n n

1 1

n-1 n-1

SWITCHCONTROL UNIT

3R3R

3R

3R3R

3R

3R3R

3R

Figure 14. The node architecture that can be simulated with SIMON.

71

a fixed length fiber delay line (FDL). There is no synchronization stage at the node input: this

is due by the assumption that the network is completely synchronized and all the packets have

a fixed length. In this way all the packets are fit into time slots, and the first stage of the node

has to be provided with a fast bit–level synchronizer to align its clock with the header within

several bits. Of course a master clock signal has to be distributed into the network.

While the packets are delayed in the first stage, the switch control unit elaborates the header

data and prepares the switching fabric. Some ports of the switch are provided with FDLs in

feedback configuration to solve contentions; all the FDLs give a fixed delay equal to one packet

slot: this solution implies a great reduction of the needed components with respect to other

architectures. For example no wavelength converters are needed to choose the right delay for a

packet as in the WASPNET solution. Moreover the complexity of the algorithm that chooses

the packet destination is very low:

• first of all it analyzes the header data and chooses the suitable output port for each packet;

• if contentions occur a packet is sent to a free FDL;

• if there are no available FDLs, the packet is dropped.

The cycle is repeated at each time slot. More details on the way to choose the output port

and the FDLs usage are given in the following sections.

When the packets arrive to the output ports of the switching fabric the signals are regener-

ated in the optical domain: this implies re–amplification of the signal, re–shaping and re–timing.

These operations can be completed in the optical domain by use of SOAs and Mach–Zehnder

72

interferometers; see section 2.2 for details. Regeneration of the signals cannot be avoided be-

cause FDLs and the switching fabric itself introduce noise: after a small number of hops the

signal could be unusable if no regeneration is provided on a hop–by–hop basis. Moreover, since

regeneration is present only at the final stage of the node, the maximum number of consecutive

recirculations allowed to a single packet has to be upper bounded: SIMON controls this number

for each packet that has to be put into a FDL. If a packet can’t be forwarded and it has reached

the recirculation limit, it is dropped. Of course this limit depends from the specific characteris-

tics of the used components: for this reason SIMON hasn’t a fixed value, but it accepts at run

time a particular number; in this way the simulator can be adapted to different components.

The switching fabric is supposed to pass transparently each payload from the input to the

correspondent output port. It can use the promising MEMs technology, exposed briefly in

section 2.1.1. Since this solution is not available at the present state of the art of the optical

components, it can be substituted with an arrayed waveguide grating (AWG): this implies that

tunable wavelength converters (TWCs) are needed before each input port of the AWG to switch

correctly the packets, as in the WASPNET project.

This kind of switch architecture can easily support packet priority: a higher class packet

can preempt packets belonging to lower classes, by sending them to the FDLs. This can be

a very important feature for the next–generation optical networks, having to support different

services with different requirements (see 2.4.3).

73

A final remark on the many types of traffic policing implemented by the CLASS nodes is

needed: since SIMON users do not perform any traffic shaping, it is clear that no traffic policing

is implemented at the node level.

3.2.4 Routing implementation

The sets of links crossed by the packets are determined automatically by SIMON for all

the flows in the network. The same algorithm used by CLASS is applied to obtain the best

performance from the given topology (see section 3.1.5 for details). But to know the link that

the packet has to cross is not sufficient to complete the forwarding operation: in fact also the

specific wavelength channel has to be chosen for each outgoing packet. For this reason the

routing algorithm adopted by CLASS has to be modified to deal with the new wavelength

domain.

ATM networks were not designed to work on networks with many different parallel channels:

if the capacity was not sufficient to carry all the traffic, the solution was to add new capacity

to the single link, not to add new links in parallel with the old one. In fact in the case of many

parallel channels the routing strategy implemented in ATM networks is pretty inefficient, as

explained in the following.

ATM associates with each couple of source–destination users a virtual circuit identifier

(VCI); therefore cells belonging to a particular stream are tagged with the correspondent VCI.

The nodes implement the routing function analyzing the VCI of each cell: a routing table in

each node contains — for all the VCIs passing through the node — the output port the cell

must be directed to. This is a kind of ’static’ routing: given the VCI, the path from the source

74

to the destination is univocally determined; therefore all the cells of a particular stream must

pass through the same links. In this way the packets can be delivered in order to the destination

users. When multiple parallel links are present this method has a high degree of inefficiency: in

fact if a cell is assigned to a particular link — and this link is occupied by another transmission

—, it can’t be forwarded using another parallel link. In other words the cells can’t exploit the

bandwidth of the parallel links different from the one assigned statically to them, even if these

links are free.

On the contrary SIMON uses a sort of ’dynamic’ routing to exploit at the maximum level the

bandwidth given by different parallel channels. The switch control unit, analyzing the packet

header, is capable to know the node the packet has to be forwarded to: in this case all the

channels that are connected to this node can be used. When contention occurs, the controller

checks all the parallel channels, and if one of them is free, the cell can be forwarded. On a

network using WDM, this action represents the searching of a free wavelength among all the

different channels in a single optical fiber.

Therefore the routing strategy implemented by SIMON can be divided in to two parts:

• before starting the simulation, SIMON runs the routing algorithm on the network topology

that must be simulated: in this way it assigns one set of channels (from the source to

the destination user) to each flow in the network, and a routing table for each node can

be built; the paths from sources to destinations are chosen using the same algorithm

developed for CLASS (see section 3.1.5);

75

• during the simulation, the node analyzes the header of each packet that must be forwarded

and reads the correspondent outgoing channel in its routing table. If it is occupied (for

example by a previously scheduled packet), the controller searches for another free channel

on the same link: the packet is forwarded to the first available one, otherwise a free FDL

is searched.

This method can be seen as a simplification of the LAUC and LAUC–VF algorithms described

in section 2.4.2, in the case of a slotted network. An example of its way of working is depicted

in Figure 15. It is assumed that:

1. the three channels belong to the same link (and that they are analyzed in the order

C1 → C2 → C3 by the controller);

2. packet P1 is arrived at instant t0, so it must be forwarded in the following timeslot,

starting from instant t1;

3. the new packet P1 must be forwarded on channel C1.

Analyzing the data contained in the header of packet P1, the controller detects a contention

on channel C1, caused by the already scheduled packet P2; so it searches a free channel on the

same link: the first available slot is detected on channel C2. At this point the output port

is determined, and the switching fabric can be prepared to create the right lightpath for the

payload from the input to the output port.

76

t

t

t

C1

C2

C3

t 0

New packet

}timeslot

P1

P2

t 1

Figure 15. An example of packet forwarding.

3.2.5 Fiber delay lines usage

SIMON nodes support FDLs to solve contentions: as seen in the previous sections, a packet

is sent to an FDL if all the channels for a particular destination are occupied. In the current

organization of the program, all the FDLs can store one packet and release it in the following

time slot: each FDL has a counter that is decreased at each time step; when it is equal to zero

the packet has finished to cross the FDL, so it has to be re–switched by the node. Therefore

the actual FDLs length is given by the initial value of the counter: this method makes SIMON

a flexible program, because it allows an easy implementation of FDLs with variable length. As

77

it can be seen, in the case of a slotted network the FDLs design is easier than in the unslotted

case (see section 2.3).

FDLs are viewed as a group of additional output ports by the network node, in which a

packet can be routed if the optimal output link is occupied by another transmission; of course

these fibers create a group of additional input ports also, that have to be taken into account

when the inputs are analyzed.

As explained previously, SIMON puts an upper bound on the maximum number of consec-

utive recirculations in the FDLs, due to noise and non–linear effects introduced in the signal

when data are recirculated (see section 3.2.3). In the following the rules adopted by the node

with respect to the packets in the FDLs are outlined.

The group of the FDLs is analyzed before the other input channels: this is because the

header of a packet inserted in a FDL is already processed — the node processes the packets

header when they arrive to the node to determine the output link —, so to forward a packet

in the FDL is computationally less expensive than to process a new one. This method gives

a certain ’priority’ to the FDLs, and this lowers the probability to drop a packet due to too

many consecutive recirculations. A single packet is forwarded if there is a free channel on the

output link. Otherwise the first available FDL is searched; if there are no free FDLs the packet

is dropped.

The FDLs of a node are examined in a fixed order, and this can affect the way the data are

processed by a node. In fact if the first analyzed FDL is number 1 and the last is number F ,

the packet in FDL number 1 will be certainly forwarded, because it is the first to be analyzed

78

(and all the output channels are surely available). The packet memorized in FDL number 2

can be immediately forwarded if its next–hop is different from the one of packet in FDL 1, or if

the link previously used contains at least two channels; otherwise it is stored in FDL number 1

(that is surely available). So the packet in FDL number 2 will be forwarded at least in 2 steps

(and after one more recirculation). Generalizing this concept the packet in FDL number F will

pass through a maximum number of recirculations (MR) equal to MR = (F − 1), and after F

steps it will be certainly forwarded. So this kind of usage of the FDL poses a natural upper

bound to the recirculations of the data.

For each node a more precise upper bound can be obtained considering the smallest number

of channels for each link, in other worlds the smallest set of parallel channels (SP ). Considering

that SP is the minimum number of packets forwarded in a single step, the number of maximum

recirculations suffered by the packet in FDL number F (the last one) must be such that the

following equation holds true:

F − SP · MR ≤ SP (3.1)

and so:

MR =

⌈

F − SP

SP

⌉

(3.2)

3.2.6 Simulation accuracy

SIMON uses the same CLASS method to determine when to stop the simulation. The

statistical module collects samples of three variables: user–to–user delay, delay suffered by the

packets in the network, and packet loss rate. Since the delay experienced by the packets in the

79

first stage of a switch is equal for all the packets, it is not considered. The only other source

of delay in the network simulated by SIMON are the FDLs: each time a packet is recirculated

the specific counter of the packet network delay is increased of one unit. In this way measuring

the delay suffered by the packets it is possible to know how the FDLs are used in the network.

CHAPTER 4

OPTICAL NETWORKS DESIGN

Simulation is a powerful method to know the performances of an already designed network;

but this approach can’t face the problem of the dimensioning of the network elements in such

a way to obtain particular results: this aspect is very important because the same elements

connected in different ways could give out very different results.

This chapter exposes a new method to design an optical network, that gives encouraging

results.

4.1 General formulation of the optimization problem

The optimization methods can be stated in many different ways, depending on:

• the data that are given to the designer;

• the parameters that must be found by the optimization method;

• what aspects of the network performances are taken into account evaluating its optimality

(the optimization criterion);

• the constraints that have to be met by the final solution.

The data that are given to the designer are often limited to the network topology: a set of

nodes connected by links; the nodes can represent the geographical location of traffic exchange

devices; the links are the physical mediums that join these different devices. The traffic matrix

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can be added to the topology: it summarizes the amount of traffic exchanged between each pair

of nodes in the network.

The constraints that must be satisfied can involve the delay experienced by the packets

into the network: it can be required that the average packet delay is maintained below a given

threshold; they can involve the network reliability: the services are supposed to be guaranteed

also in the case of a certain number of links and node failures; a third possibility is that the

total cost of the network would be contained below a maximum investment. Of course there is

the possibility that multiple constraints must be satisfied simultaneously.

The optimization criterion is of course one of the most important aspects in the network

design: typically it is formulated in terms of minimization or maximization of some parameters,

such as the packets delay or the average throughput of the network.

The final aspect that contributes to differentiate the optimization problems among them is

represented by the parameters that must be found. For example it could be asked to add links

to a set of given nodes: in this case the optimization problem regards the topology design; the

designer can choose the pairs of nodes that must be joined, and the capacity of each link: this

is a problem that must be faced when a new network is built, and only the positions of the

terminals are fixed. It is also possible that the links are already put in the network topology,

and the designer must calculate only the capacity: the topology is more complete than in the

previous cases, and this happens when an already working network must be re–organized to

support different traffic flows; this situation is known as ’the capacity assignment’ problem. A

third situation is represented by the so called flow–model problem: in this case the topology is

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completely given, and the designer has to choose the paths that the packets have to follow in

the network.

Therefore the problem of optimization of a network is far from being easy: it involves many

aspects, and the ways to obtain an optimal solution are often so expensive in terms of calculation

efforts to be not practicable. For these reasons a lot of the proposed optimization methods

are based on heuristics (Bertsekas and Gallager, 1992): in this way there is no guarantee to

find the optimal solution, but if the results are good, the method can be accepted as a good

approximation to the problem solution.

Sometimes the optimal solution can be found through an analytical way; this is the case of

the classical capacity assignment problem: given a fixed topology — the set of links connecting

different nodes — there are well known formulas (Butto et al., 1991) to find the optimal capacity

of all the network links such that the average packet delay is minimized, and satisfying a specific

constraint on the total network cost. If the capacities can take all the possible values (this means

that the results are usually real numbers), the problem can be easily solved in an analytical way.

On the contrary, if the capacities must be chosen in a set of predefined values, the solution can

be found through integer linear programming methods, which are complex, hence applicable

only for networks of small dimensions.

4.2 Particularities of optical networks

In the particular case of an optical network, the situation is slightly more complicated. For

what regards the data that are given to the designer there is no difference from the general case:

the set of nodes and links can be specified as usual, and also the traffic matrix. The aim of the

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optimization can also be specified as before: it is possible to choose to maximize the average

throughput, or to minimize the packet delay. The main differences given by considering an

optical network can be noticed in the parameters that must be found by the designers, and in

the constraints imposed on the solution.

Given a topology, it would be useful to know how many wavelength channels must be put on

each link. In fact the bit–rate of each channel is defined by modulation schemes, transmission

codes and other parameters that are not likely to be affected by the designer; on the other

hand the flexibility given by the WDM technology easily permits to assign a different number

of channels to each fiber, resulting in a different capacity of the link. This is a kind of capacity

assignment problem (as previously described), but in this case the solution must be found in

the integer domain: all the links can have only an integer number of channels, so that their

resulting capacity must be multiple of the basic channel capacity.

The other main difference with respect to the general problem is represented by the way

to interpret the cost constraints that must be met by an optical network. In fact the cost of

an electronic–based network can be calculated as a linear function of the capacity of the links:

more speed in the links implies that the switches have to accelerate the processing time, and

consequently the internal components have to be changed to allow a faster passage of the data;

the direct consequence is a rise in the costs of the switch. This method to calculate the cost is

not suitable for the networks based on optics: in fact in a first approximation rising the capacity

of a link (by adding new channels) basically implies a variation in the number of ports of a

node, and only a slightly modification in the internal components; one node port is intended to

84

correspond to one wavelength: in the case of the switch depicted in Figure 14 the number of

ports is (n + m). The cost of an optical cross connect is mainly determined from the number

of transceivers, and only secondarily by the internal complexity: therefore the variation of the

capacity of the links affects in a different way the total cost of the network. In conclusion the

main quantity that must be taken into account in an optical network in order to determine its

cost is the number of ports of the switches.

In addition it must be observed that the technology adopted in the nodes strongly affects

the cost function of the network: if MEMs technology is adopted to build the switching fabric,

a variation in the number of ports does not implies a great variation in the cost, because MEMs

offers a great scalability from this point of view (see section 2.1.1). On the other hand, if the

switching fabric is done using AWG, the scalability is different, and some thresholds on the

number of ports can’t be overcome, imposing the necessity to cascade multiple stages if the

number of ports increases too much. This implies that the cost function has to be carefully

chosen, depending on the adopted technology.

Having this in mind, it is possible to find some new constraints thought specifically for the

optical networks. In fact if the designer has a constraint on the total cost of the network, he/she

can transform this value to the total number of ports counted by the network. As explained,

the overall cost of an optical network is concentrated in the nodes, because the cost of the

fibers is negligible, with respect to the components that constitute an optical switch (see also

section 1.2.1). Moreover the number of components in a node (and consequently its cost) are

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proportional to the number of ports of the node. Therefore the total cost of an optical network

strongly depends from the sum of the number of ports of all the nodes.

But there is another aspect that must be taken into account: the cost of a switch is not linear

with its number of ports. Considering the total number of ports in a network as a constraint,

there is the possibility that some nodes of the found solution (the optimal one) would have a

lot of input/output ports: this fact rises the cost of the network because an high number of

ports in a single switch means high cost, whatever technology is adopted. A possible way to

consider this effect is to pose an upper bound on the number of ports for each switch: of course

this solution limits the total number of ports also, so it is very precise in the definition of the

network cost.

Therefore from the previous observations it is easy to understand that if the network cost

is limited, a possible constraint is to provide an upper bound on the total number of ports in

the network, but a more accurate design can be obtained posing a constraint on the number of

ports of each node.

4.3 Optimization problem faced in this work

Optimization problems with a number of variants were introduced in the previous section.

When developing a method to design a network, there is the needing to choose the parameters

that have to be optimized, the constraints that must be respected by the particular network,

and the quantities that must be minimized or maximized.

Seeing the opportunities that optics can give to the telecommunication market today, a

particular importance is taken by the problem of upgrading an existing topology from an

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electronic–based architecture to an optical one, using WDM. In this case some assumptions are

made on the general optimization problem:

• It is supposed that the topology of the network is known; in particular, the set of nodes

and the set of links are specified to the designer: all the links indicated in the topology

must count at least one wavelength channel in the optimized solution. If there is a link

between two nodes, it is supposed that there is another link going in the opposite direction

with the same number of wavelength channels: this ensures that all the switches have the

same number of input/output ports. In addition, the traffic matrix is also available: it

contains the information on the traffic (in packets/sec) for each user pair. These data are

reasonable, because the traffic supported by an existing network is well monitored, and

the physical media are already laid in the ground; there is also the possibility that the

links are already represented by optical fibers: the existent architecture could belong to

the first–generation of optical networks (see section 1.4).

• The architecture of the nodes has to be considered: this is required to have a precise

reference for the cost function, and to be able to model the network in a proper way. In

this case the node structure supported by SIMON is chosen (see Figure 14).

• The number of wavelength channels that has to be put on each link must be found. WDM

technology offers the possibility to allocate many channels on each fiber, and this number

can affect heavily the network performances.

• The obtained network must have maximum throughput with respect to the other possible

solutions: this is a reasonable optimization criterion, widely used in network design.

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• The cost of the network must be limited. Having in mind all the considerations given in

section 4.2, it is assumed to have all the switches with a fixed number of input/output

ports, implemented with the same technology.

At this point the problem that has to be solved is completely defined. It requires to maximize

a measure on the whole network (the throughput) changing the number of WDM channels on

the links. Therefore the first part of the solution is to find the relation that holds between the

parameter that must be maximized and the number of channels on the links. This task can be

achieved in two steps:

• expressing the performance of a link as a function of the number of WDM channels present

on that particular link;

• finding the relation between the performances of the whole network and the single links.

These two steps are analyzed in detail in the following.

4.3.1 Modeling the links

To find how the number of WDM channels affects the performances of a link, it is necessary

to model each link in a proper way: a possible solution is to use the classical queueing theory.

Each output link of a node can be seen as a queue with a different number of servants and

a buffer: the number of servants is equal to the number of wavelength channels present on

the link, and the buffer can be represented by the fiber delay lines of the node. The arrivals

in the queue depend from the type of users present in the network: in the case of type one

users (described in section 3.2.2) the time distribution between two consecutive arrivals forms

88

a Poisson process. If all the packets have equal length, the node takes a constant period of

time to forward the packet: this time is equal to the delay introduced by the first stage of

the node, needed to process the packet header; under this hypothesis, the service time of the

queue can be seen as a particular case of an exponential probability density. Having in mind

all these considerations, and referring to the well known Kendall notation (Marsan and Neri,

2001), it is possible to conclude that a link can be modeled as a M/M/L/k queue, where L is

the number of servants and k the length of the buffer; the first and the second M represent the

time distribution of the arrivals and the probability distribution of service times, respectively.

Consequently the node can be modeled as in Figure 16, where a node with three input/output

ports is depicted: there is a battery of servants for each link, and the number of servants in

each group is equal to the number of wavelength channels on the specific link.

It can be observed that this model is not perfectly adherent to the reality. First of all the

fiber delay lines of a node can’t be represented as a simple buffer, because there are many

differences between FDLs and RAMs, as explained in section 1.2.3: the packets must be re–

routed when they reach the end of the FDL, and after some recirculations they must be dropped

due to noise insertion. This means that in the case of FDLs the storage time is a matter of

concern, both in the short, both in the long period. The second limitation of the adopted model

is due to the fact that each link has not k FDLs on its own (as it is supposed in the M/M/L/k

model), but the set of the FDLs is shared among all the links. In spite of these limitations, this

is a relatively simple and accurate model that can be built using the classical queueing theory.

89

Input

Queue

Servants

Output

Figure 16. The node model using queueing theory.

90

If it is not possible to make a more accurate model, the network structure can be modified to

resemble the behavior of this model. Since one of the differences is represented by the necessity

to discard the packets after a maximum number of recirculations (called MR parameter), it

could be provided a 3R regeneration after each passage in the FDLs: in this way the MR

parameter could be put equal to infinite, and the FDLs operate more similarly to a buffer. In

other words running a simulation with MR that goes to infinite means to have a network that

is more similar to the developed model.

Adopting this model, and supposing the separation of the buffers among the different output

fibers, it is possible to calculate analytically the packet loss probability for a particular link. It

results equal to (Butto et al., 1991):

P = πL

( ρ

L

)k

= π0

ρL

L!

( ρ

L

)k

(4.1)

where ρ is the offered traffic expressed in Erlang. Of course each buffer is supposed to be

sufficiently long to contain the queued packets. Assuming that the average traffic on a link is

α packets in a second, it can be calculated as:

ρ =α

µ(4.2)

where µ is the capacity of the link in packets/s; the α coefficient represents the traffic load on

the considered link, and it can be obtained by the traffic matrix, as explained in section 4.4.1.

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If the links counts L wavelength channels, each carrying C bps, and the packets are l bits long,

the µ parameter can be calculated as:

µ =L · C

l(4.3)

In Equation 4.1 the quantity π0 can be obtained as:

π0 =

[

L−1∑

i=0

ρi

i!+

ρL

L!

1 − rk+1

1 − r

]−1

if r 6= 1

[

L−1∑

i=0

ρi

i!+

ρL

L!(k + 1)

]−1

if r = 1

(4.4)

where:

r =ρ

L(4.5)

It is important to note that the packet loss probability is equal for all the WDM channels

belonging to a single link. At this point the relation between the number of WDM channels

and the packet loss probability of the link is found.

4.3.2 Modeling the network

The second step is to find how the performances of the single links can affect the overall

performance of the network. The following quantities can be defined:

• P i: packet loss probability on link i.

• F : set of all the flows present in the network. A flow is uniquely determined by a

source/destination user pair; all the packets with the same source and the same destination

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belong to the same flow. If there is no traffic exchanged between two users, that particular

flow is absent; consequently the number of elements of this set is equal to the number of

non–zero elements of the traffic matrix.

• tf : average traffic of flow f ; these coefficients are indicated by the traffic matrix, and they

are supposed to be expressed in packets/s.

• Lf : set of all links traversed by flow f . This set is determined by the routing algorithm

present in the network.

• Pf : packet loss probability for flow f .

• Ptot: packet loss probability of the whole network.

To extend the vision from the particular link to the whole network, it is useful to start with

an illustrative example. A simple network is reported in Figure 17: supposing only one flow

(labeled as number one) exchanged between user one and user two, and knowing the packet

loss probabilities P 1 and P 2 of the two links, it is immediate to obtain:

P1 = 1 −(

1 − P 1)

·(

1 − P 2)

(4.6)

that represent the packet loss probability for the flow from user one to user three. The losses

on the two links are supposed to be independent.

In the case of a generic topology, the previous expression can be generalized:

Pf = 1 −∏

i∈Lf

(

1 − P i)

(4.7)

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u1 u2

Node

User

l1 l2

Link

1 2 3

Figure 17. Simple network example.

to obtain the packet loss probability for the generic flow f .

The performance of the whole network can be found easily as the weighted average of all

the flows present in the network:

Ptot =

∑

f∈F

tf · Pf

∑

f∈F

tf(4.8)

This formula gives an estimate of the performances of all the network, knowing the number of

WDM channels on each link. This result is confirmed also by a recent study (Yao et al., 2001b).

4.4 Optimization algorithm

To solve the optimization problem stated in section 4.3, the expression reported in Equa-

tion 4.8 has to be minimized with respect to the number of WDM channels present on each link;

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in addition the constraint on the maximum number of ports for each node must be satisfied,

and the solution must be represented by natural numbers (real values for the number of WDM

channels are of course not acceptable). In addition assuming the hypotheses of section 4.3 each

link must have at least one channel: in fact a link with zero channels implies a modification of

the topology, because the link is canceled and no longer exists in the real network; as stated

before, the topology is one of the data of the problem, and it can not be modified by the

optimization method.

For these reasons an analytical solution can’t be found for a problem of this kind: the

alternative way is to adopt an heuristic approach.

Some simple observations can be useful to simplify the problem. Each couple of input/output

ports of a node can be connected to two WDM channels (in this case they are used to forward

packets), or by the beginning and the end of a FDL (to solve contentions). If a node has a free

(not already used) couple of ports, and no new channels can be added (because the neighbor

nodes can’t accept new connections, for example), there is the possibility to add a new FDL.

Since a node with one more FDL gives better performances — because the new FDL can solve

some contentions, without adding any disadvantage —, it is convenient to use the free ports of

a node to allocate new FDLs. Therefore it is useful to avoid unused ports in any node of the

network, and the best performance will be obtained by a network in which all the nodes have

the maximum number of ports: of course this conclusion is a direct consequence of considering

an equal number of ports for all the network nodes.

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This conclusion is also confirmed by another intuitive observation: a node port has a cost,

so a free port means waste in allocation of the available resources; as explained before, in this

case it is always possible to use the free ports (at least allocating new FDLs), so that in the

optimized network all the nodes must have the maximum number of ports.

If all the ports of the nodes must be used, the optimization problem requires to find the

proportion between the ports dedicated to wavelength channels and the ports connected to

FDLs that gives the maximum performance. This proportion must be found for all the nodes;

in addition, the number of wavelength channels for all the links has to be determined also.

Having this in mind, it is possible to differentiate the optimum topologies accordingly to the

different use of the node ports: to offer capacity (adding channels on the links) or to offer

more possibility to solve contentions (adding FDLs); with this criterion a solution with an high

number of FDLs is opposite to another one that counts many more channels on the links. The

optimum solution has to be found in the ’space’ of the possibilities: one extreme of this space

is represented by the topology with only one channel for each link (minimum connectivity,

maximum number of FDLs); the other one is the topology with the highest number of channels

on each link. Of course in the latter case some FDLs can be present: if a node has some free

ports and further channels can’t be added (because all its neighbors have reached the maximum

number of ports already), then some FDLs can be added to the node.

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4.4.1 Starting point

The heuristic approach used to solve this problem starts with an initial topology, then

perturbs it to obtain a better performance. When no further improvements can be found, the

search is stopped.

The initial topology that the algorithm must modify is placed in one extreme of the space

of the possibilities described in the previous section: in particular it is a maximum connected

topology: the number of wavelength channels for each link are limited only by the number of

input/output ports of the nodes; the number of FDLs in the entire network is therefore reduced

to the minimum possible value. This is only one of the many possible starting topologies: a

discussion on the results obtained choosing another initial topology is given in section 4.4.3.

The maximum connected topology can be obtained in two steps:

• calculating the minimum number of ports needed for each link, using the traffic matrix

data;

• adding channels on the links, until the constraint on the maximum number of ports is

respected, to obtain a network with the highest possible capacity.

This procedure ensures to give the right importance to the different links: a higher number

of ports is assigned to a more utilized link; then channels are added to all the links until the

constraint on the number of ports of the nodes permits the operation. These steps are explained

in the following.

The traffic matrix is used to calculate the minimum number of channels for each link.

To know the total traffic on a link, the contributions of the flows that pass through the link

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are summed, then the minimum number of ports is calculated. Recalling the quantities of

section 4.3.2 and defining:

• F i: the set of flows passing through link i;

• αi: the average traffic on link i in packets/sec;

it is possible to write:

αi =∑

f∈F i

tf (4.9)

Therefore the minimum number of ports ni for link i is given by:

ni =⌈αi

C

⌉

(4.10)

where a single WDM channel has a capacity equal to C bps. At this point all the links have a

number of channels sufficient to carry all the traffic.

The second step in obtaining the starting topology is to assign the maximum number of the

remaining ports to new wavelength channels on the network. To determine if a single channel

can be added to a link, two nodes are analyzed: if the node from which the link comes out

(source node) and the node where the link terminates (destination node) have at least one input

and one output ports not already connected, then one channel can be added to the considered

link. The channels addition is performed in this way:

1. a list of all the links between the nodes is created (the links from/to the users are excluded):

the order of insertion in the list is random;

98

2. starting from the beginning of the list, each link is analyzed: if it is possible one channel

is added to the link, then the following one in the list is processed;

3. arrived at the end of the list, if at least one channel was added to any of the links in the

last scan of the list the previous point is repeated; in the other case no more channels can

be added to any of the links, thus the addition algorithm is stopped.

This way of operating ensures to avoid completely contentions assigning the ports: the list

of the links is analyzed many times in the same order, and only one channel is added to a

particular link at a time. Therefore an equal distribution of the free ports of a node among all

its input/output links can be obtained.

If there are still free ports, the proper number of FDLs is added to each node to reach the

limit. With this operation the starting topology is completely defined; it is important to note

that there are no free ports in any node of the network, so this topology represents a good

starting point. Another interesting property is represented by the fact that it is a maximum

connected network: the number of ports dedicated to the wavelength channels can’t be increased

anymore, so the optimal solution must be found trying to remove capacity, and consequently

adding some FDLs.

At this point the algorithm calculates the packet loss probability for each link (using Equa-

tion 4.1) and for the entire network (using Equation 4.8).

4.4.2 Iteration steps

To find a new topology with a better performance (a lower packet loss probability) than the

current topology the algorithm performs the following steps:

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1. Two links (going in opposite directions, therefore connecting the same couple of nodes)

where two channels (one for each link) can be removed are searched. It is required that

the considered links have a number of channels greater than the minimum threshold

(determined through Equation 4.10) required to carry the traffic specified in the traffic

matrix.

(a) When two links that satisfy the previous condition are found the number of FDLs

in the source and destination nodes is increased of one unit, and two channels are

removed; the packet loss probability is recalculated for all the links of the source

and destination node (because changing the number of FDLs in a node affects the

performance of all the links, not only where the channels are added) and for the

entire network also (Equation 4.8). In this way it is possible to compare the total

packet loss probability of the new topology with respect to the old one.

(b) The link that gives the best improvement in performances (when two channels are

removed, and the corresponding FDLs are added) is memorized: this improvement

is calculated as the difference between the network packet loss probability of the

current topology and the new one. The links that correspond to the biggest difference

is memorized.

2. At this point the best new topology (obtained adding FDLs and removing capacity) is

known. If its packet loss probability is lower than the current one, then it can be adopted:

in fact in this case removing two channels on the specific link means to obtain a better

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result. On the contrary, if the new topology has a bigger packet loss probability, then it

is proved that the perturbed network does not represent a real advantage with respect to

the current situation: in this case there is no further improvement removing capacity, so

the best solution is found.

In this way it is possible to calculate the number of WDM channels for each link, and

consequently the number of FDLs for each node, completing the design of the network.

As it can be seen, the described optimization method uses the traffic matrix to calculate

some quantities, such as the minimum number of ports in the starting topology, and the packet

loss probability of the whole network. As a result, the design of the optimum network depends

from the traffic matrix: changing it, very different solutions can be obtained.

4.4.3 A note on the implementation

Of course it is possible to search the solution that gives the best throughput following a

different way: in particular, the starting topology that must be perturbed by the optimization

algorithm (described in the previous sections) can be modified. As stated in section 4.4 the

described heuristic approach searches the minimum of a non–linear function in the space of

the possibilities: changing the starting topology is equivalent to change the initial point in this

space from which the algorithm starts. Since it is not ensured that the heuristic approach finds

the global minimum of the analyzed function, but only a local minimum, a different initial point

could drive the algorithm to another solution; in this way different local minima can be found,

and then the point giving the best result can be chosen. The technique of changing the starting

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point is a well known method to improve the performances of a heuristic approach, as shown

in (Bertsekas and Gallager, 1992).

In this case a possible alternative in the choice of the initial starting point for the optimiza-

tion algorithm is represented by a minimum connected topology, placed at the opposite side

in the space of the possibilities with respect to the maximum connected topology used before.

It can be obtained as explained in section 4.4.1, allocating the minimum number of ports for

each link. Consequently the iteration steps described in section 4.4.2 must be modified: the

algorithm, perturbing the topology, tries to add two channels to two links (going in opposite

directions), and at the same time two FDLs are removed in the source and destination nodes;

then the calculation of the total packet loss probability of the network is made in the same way

as before. Adopting this method the number of channels can be increased step by step, until

the optimum solution is found.

This new approach was developed in the cases analyzed in this thesis (see chapter 5), but the

found solutions were always worst than the ones obtained starting from a maximum connected

topology: for this reason only the results derived from the method exposed in section 4.4.2 are

reported.

4.5 Optimization program

It is useful to implement the described algorithm on a computer program, to make easier

and faster the analysis of a network. This section describes the program developed in this work.

The optimization program must know the network topology to operate on it. To make easy

the description of a network it was chosen to adopt the same RC language (described in section

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3.2.1) used by SIMON: in this way the optimization program reads an RC file and it can know

all the aspects of the network; since the number of channels for each link is not already known,

the input file must provide only one channel for each link (the described network is minimum

connected).

The second data that the program needs to know are represented by the traffic matrix

elements: using the RC language the program knows the number and location of the users, so

that it can calculate the traffic matrix automatically running the same routing algorithm used

by SIMON (see section 3.2.4). The default paths of the flows through the network calculated

by the program can also be overridden specifying different paths in the RC file, as explained in

section 3.1.5: in this way the desired traffic matrix can be obtained in a transparent way. Of

course, to calculate the amount of traffic produced by each user, the program requires the total

network load. Other input data required by the program are the capacity of each channel and

the number of input/output ports of each node.

Summarizing, the optimization program works as follows:

• the total network load and the RC input file are specified as command–line options;

• the program analyzes the RC file to control the syntax and the correctness of the network

topology;

• if the RC file is correct, the capacity of each channel is asked to the user (this is the first

run–time parameter);

• the paths of the flows through the network are calculated; the paths specified in the RC

file are also considered; then the traffic matrix is calculated;

103

• knowing the traffic matrix, the minimum number of ports for all links is calculated;

• the cost constraint (the number of input/output ports for the nodes) is asked to the user

(second run–time parameter);

• the starting topology is found: the free ports of the nodes are dedicated to add capacity

in the network; if this is not possible, new FDLs are added;

• the optimization algorithm is started;

• the final configuration is printed out;

• a new RC file describing the optimized topology is written.

With this program the design and analysis of a network is very easy; it is well integrated

with SIMON, and the two programs can be used together to carry out a precise analysis of a

general topology:

1. a RC file describing the general topology of the network is written by the designer (only

one channel for each link is listed);

2. this RC file is used as input for the optimization program: a new RC file with the optimized

topology (different number of channels for each link) is the output;

3. the obtained RC file can be used to run a simulation with SIMON;

4. the performances of the optimized topology can be compared with the results of other

simulations obtained with SIMON using different RC files.

Examples of this way of operation are reported in the following chapter.

CHAPTER 5

SIMULATIONS

This chapter shows some simulation results that can be obtained using SIMON as a power-

ful tool to study the network performance. In addition, the results derived by the optimization

method described in the previous chapter are reported for the different types of analyzed net-

works.

5.1 Network topologies

The topologies considered in this work are chosen for the great importance that they have

in the current networking framework. The first analyzed topology is a simple network made

of eight nodes. As it can be seen in Figure 18 the four central nodes represent the network

backbone (namely ’the core’) and the other nodes (labeled as ’boundary nodes’) play the role

of the access to the inner structure. It will be labeled as topology number one in the following

for sake of simplicity.

Another topology studied in this work is the USA backbone, represented in Figure 19: it

counts 28 nodes and 90 links; it will be called topology number four in the following. Topology

number five is the label for the structure depicted in Figure 20. These networks represent two

significant backbone structures that were studied in the literature (Kazutaka and Kim, 1995).

104

105

Boundarylinks Core

links

Node

User

1 2

34

5

6 7

8

9

1011

12

Figure 18. General backbone structure (topology number one)

106

28

27

26

25

24

23

22

21

20

19

1815

1611

10

12

13

17149

85

6

7

3

42

1

Figure 19. USA backbone structure (topology number four)

Each node in the analyzed network can have one or more users attached to it. It’s also

possible to have nodes with no users; the characteristics of the traffic generated by the users is

described case by case.

For what concerns the graphs organization, the x axis represents the total network load,

that is the sum of the traffic produced by all the users; the y axis shows the fraction of packets

successfully transmitted (for the throughput graphs) or the average network delay suffered by

the packets (for the delay graphs).

The throughput of the network is calculated as the number of packets successfully received

over the total number of generated packets; in this case the packets present in the network

buffers when the simulation is stopped are considered lost (because they are already generated,

107

6

1

2 3

4

5

78

9 10

11

Figure 20. Topology number five

108

but not yet received). This fact can have only a minor impact on the overall result for the

types of networks analyzed here: the only buffers are represented by FDLs, whose number is

small with respect to the total number of packets generated during a simulation; the length

of the simulation is driven by the statistical modules, that ensure a minimum confidence level

for the quantities under observation (as explained in section 3.1.6): therefore an arbitrary

number of packets generations can be achieved setting the corresponding parameters; in this

way the measure of the throughput can reach the desired level of accuracy. The network

delay is expressed in timeslots: each packet, along its way to the destination, can suffer some

recirculations in the FDLs of the nodes: each recirculation increases the counter of the network

delay for the specific packet. Therefore in the graphs only the delay suffered by the packets inside

the network is reported: to obtain the end–to–end delay (from the source to the destination

user) the delay in the access network (see section 3.2.1) must be added.

If more than one line is present on a graph, they can be distinguished by mean of different

symbols: a legend explains the parameters that distinguish the curves. In detail, if the legend is

in the form nλ, where n is a number, then the line corresponds to a topology with n wavelength

channels on all the links. If the legend is in the form M/M/L/k the simulation is conduced

on the topologies obtained through the optimization method described in chapter 4. There is

the possibility that the maximum number of consecutive recirculations (MR) allowed during

the simulation is indicated also; if the MR parameter is not indicated then it is equal to the

maximum allowed value (four for all the cases reported in this chapter). If the legend indicates

’n users’, then the correspondent topology counts n users. The different wavelength channels

109

on a link are also indicated as ’colors’, referring to the physical light pulses used in the optical

fibers.

All nodes are designed to have a fixed number of input and output ports. The number of

fiber delay lines (FDLs) that are present in a node is calculated as the difference between the

total number of ports of the node minus the number of wavelength channels attached to the

node. For example a node with 24 input/output ports, having five links, and four wavelength

channels on each link, has exactly four FDLs (because 20 ports are occupied by the wavelength

channels).

5.2 Maximum recirculation times

This section shows the results of the simulations aimed at investigating the optimum num-

ber of times a packet can be recirculated in one FDL consecutively (MR parameter): many

simulations can be performed changing this parameter, to measure its impact on the network

performances. Usually allowing an higher number of recirculations the performances are better

for all the network loads, as expected. This rule is broken in the case of topology number one

(Figure 18): supposing that all the links have two wavelength channels each, at high loads the

maximum throughput is achieved in the case of two consecutive recirculations, as it can be

seen in Figure 21. On the contrary in the case of intermediate loads the highest throughput is

achieved with the highest number of recirculations. The average delay suffered by the packets is

shown in Figure 22: the higher is the network load, the higher is the delay, because contentions

occur more frequently when there are many packets in the switches. In addition it is possible

to notice that the delay is proportional to the maximum number of consecutive recirculations

110

0 5 10 15 20 25 300.4

0.5

0.6

0.7

0.8

0.9

1

Total network load [Gbps]

Fra

ctio

n of

pac

kets

suc

cess

fully

tran

sfer

red

TOPOLOGY 1. Poisson constant traffic (type 1). 2 λ

1 RC2 RC4 RC5 RC8 RC

Figure 21. Throughput of topology one in the case of different maximum recirculation times.All the links have two channels.

(MR parameter): allowing more passages in the FDLs to a single packet the delay is naturally

increased.

5.3 General backbone topology

In this section the results relative on the topology number one are reported: the simulations

are conduced with different types and different number of users.

5.3.1 Type one users

In this case the users produce packets of fixed length with interarrival time regulated by a

Poisson constant distribution, dividing their traffic in equal parts among all the other users:

111

0 5 10 15 20 25 300

1

2

3

4

5

6

7

Total network load [Gbps]

Pac

kets

net

del

ay

TOPOLOGY 1. Poisson constant traffic (type 1). 2 λ

1 RC2 RC4 RC5 RC8 RC

Figure 22. Delay of topology one in the case of different maximum recirculation times. All thelinks have two channels.

112

they are type one users as described in section 3.2.2. All the network nodes have 16 input and

output ports, each supporting traffic at 1 Gbps.

The results are parameterized with respect to the number of different channels on each link.

Since the maximum number of ports of a node is fixed to 16, and the core nodes have four links

each, the maximum number of different channels that can be placed on the links is four. In

fact in the case of five colors the core nodes requires a minimum of 20 ports.

The network throughput achievable simulating one user for each boundary node (for a total

of eight users) is shown in Figure 23, and the average packet delay in Figure 24. Supposing a

fixed number of wavelength channels for each link, it’s easy to see that in the case of low loads

the optimal solution is to adopt three colors for each link to maximize the fraction of packets

successfully transmitted; this solution gives also an acceptable delay. The shown results are

obtained simply using SIMON on the same topology, using different RC files.

The performances of the topologies designed with the optimization method described in

chapter 4 are also reported on the graphs (in the lines labeled as M/M/L/k): each point of

these lines is obtained simulating with SIMON the optimum topology calculated for the specific

network load. In fact the result of the optimization algorithm is a function of the traffic matrix

(see section 4.4.2), so different topologies are simulated for different network loads. Since in

this case the number of colors on the links is variable, a list of the number of channels for each

link and the number of FDLs for each node is reported in section C.1.

As it can be seen in the graphs the optimized topology gives the best performances for all

the network loads. This is a good result, that proves the effectiveness of the heuristic approach

113

0 2 4 6 8 10 12 14 16 180.85

0.9

0.95

1

Total network load [Gbps]

Fra

ctio

n of

pac

kets

suc

cess

fully

tran

sfer

red

TOPOLOGY 1. Poisson constant traffic (type 1). 8 users

1 λ 2 λ 3 λ 4 λ M/M/L/k (4 MR) M/M/L/k (∞ MR)

Figure 23. Throughput of basic configuration.

followed in the algorithm. In particular, two curves are plotted using the same optimum topolo-

gies: the former is obtained limiting the number of consecutive recirculations (MR parameter)

to the maximum allowed value in a real case (assumed equal to four), while in the latter the

noise problems are not considered, therefore no bounds are put on MR. In this case the two

lines are not distinguishable, because the MR parameter does not affect heavily the performance

of the network, but in general the second method gives better results, because some packets

can be always forwarded, with no care about the recirculations suffered in a node; it is useful to

notice that it does not represent a real simulation, because to allow infinite recirculations a 3R

regenerator should be provided for each FDL: on the contrary in the node structure supposed

114

0 2 4 6 8 10 12 14 16 180

1

2

3

4

5

6

7

8

9

Total network load [Gbps]

Pac

kets

net

del

ay

TOPOLOGY 1. Poisson constant traffic (type 1). 8 users

1 λ 2 λ 3 λ 4 λ M/M/L/k (4 MR) M/M/L/k (∞ MR)

Figure 24. Delay of basic configuration.

here the signal is regenerated only in the final stage of the forwarding process (refer to Fig-

ure 14). The performances obtained with MR equal to infinity are reported for completeness,

in fact they are closely related to the model used in the optimization method: the optimization

algorithm models the nodes using the classical queueing theory (see Figure 16), so the FDLs

are described as a buffer: if MR is equal to infinite, one of the differences between FDLs and a

buffer is canceled, and the simulation results are more adherent to the performances calculated

by the optimization algorithm (see also section 4.3.1). In spite of the model limitations, the

results shown in Figure 23 are better than the ones with a fixed number of wavelength chan-

nels for each link also in the case of MR equal to four: thus the proposed optimization model

115

0 5 10 15 20 25 300.7

0.75

0.8

0.85

0.9

0.95

1

Total network load [Gbps]

Fra

ctio

n of

pac

kets

suc

cess

fully

tran

sfer

red

TOPOLOGY 1. Poisson constant traffic (type 1). 4 λ

8 users 16 users32 users64 users

Figure 25. Throughput of topology one with different number of users. All the links have fourchannels.

can be usefully used to design the colors distribution in an all–optical network. In addition it

can be noticed that the two optimum curves are very close in this case, in fact they can’t be

distinguished in the graphs.

Using SIMON it is easy to change the number of users: this allows to analyze the behavior

of the network with different configurations of the access network (represented by the placement

of the links from/to the users, as explained in section 3.2.1). The results in the case of a fixed

number of wavelength channels (in particular four colors) on each link are reported in Figure 25

and Figure 26.

116

0 5 10 15 20 25 300

0.5

1

1.5

Total network load [Gbps]

Pac

kets

net

del

ay

TOPOLOGY 1. Poisson constant traffic (type 1). 4 λ

8 users 16 users32 users64 users

Figure 26. Delay of topology one with different number of users. All the links have fourchannels.

The simulation shows that the best throughput can be obtained with four users for each

boundary node — in this case the total number of users in the network is equal to 32 —;

adopting this solution, the average packet delay is also the minimum one, meaning that the

designed access network works well for all the aspects of interest. This is due to the fact that

with 32 users the contentions in the boundary nodes are reduced than in the other cases, so

that the maximum throughput can be achieved.

117

5.3.2 Simulation with ON/OFF users

The same simulations described in the previous section are performed changing the type of

traffic produced by the users. In this case the users generate packets at constant rate during

the ON period (like type one users); ON and OFF periods are distributed geometrically with

equal average duration.

In general considering the same topology and the same network load the network throughput

obtained with this kind of traffic is higher than the one reported in the previous sections:

therefore with this simulations it is proved that the traffic type can influence the throughput in

a significative way. Furthermore it can be observed that simulating different cases for a single

topology, the relative positions of the different curves are not changed with respect to the case

with type one users: for this reason the results relative to type five users are not reported here.

5.4 USA backbone network

The network simulated here (see Figure 19) represents the USA backbone structure. It is

simulated with one user for each node (a total of 28 users) and with four users for each node

(a total of 112 users); the users generate traffic at constant rate according to a Poisson process

(type one users). All the nodes have 16 output ports with capacity equal to 1 Gbps; since some

nodes have five neighbors in this topology, the maximum number of usable colors (in the case of

a fixed number of channels for each link) is three. The results are not so different if the number

of users is changed, so that only the graphs with 28 users are reported for sake of brevity.

The network throughput and average packet delay are reported in Figure 27 and Figure 28

118

0 5 10 15 20 25 30 35 400.86

0.88

0.9

0.92

0.94

0.96

0.98

1

Total network load [Gbps]

Fra

ctio

n of

pac

kets

suc

cess

fully

tran

sfer

red

TOPOLOGY 4. Poisson constant traffic (type 1). 1 user each node

1 λ 2 λ 3 λ M/M/L/k (4 MR) M/M/L/k (∞ MR)

Figure 27. Throughput of topology four, one user for each node.

respectively. The throughput of the different topologies at low network loads is reported also

in Figure 29.

Without considering the optimization results, in a first approximation it seems that higher

the capacity in the network (and lower the number of FDLs), higher the network throughput

(see Figure 27): in fact the results of the case with three colors are better than the cases with

one and two colors at high network loads. More in detail (see Figure 29) it is possible to note

that the solution with two channels for each link gives a better throughput at low network loads:

therefore it is intuitive that an optimum solution can be found. This hypothesis is confirmed

by the optimization method: assigning a variable number of channels the throughput can be

119

0 5 10 15 20 25 30 35 400

0.5

1

1.5

2

2.5

Total network load [Gbps]

Pac

kets

net

del

ay

TOPOLOGY 4. Poisson constant traffic (type 1). 1 user each node

1 λ 2 λ 3 λ M/M/L/k (4 MR) M/M/L/k (∞ MR)

Figure 28. Delay of topology four, one user for each node.

further improved. A list of the number of channels for each link and of the number of FDLs

for each node is reported in section C.2.

5.5 Topology number five

The last simulated topology is depicted in Figure 20, using type one users and with one

user for each node.

The number of ports for each node is equal to 24: this allows to use up to three colors on

each link — in the case of using the same number of channels for each link —, because there

is one node with eight neighbors; the capacity of each port is equal to 1 Gbps. The results for

the network throughput and the packet average delay are reported in Figure 30 and Figure 31.

120

4 5 6 7 8 9 10 110.99

0.991

0.992

0.993

0.994

0.995

0.996

0.997

0.998

0.999

1

Total network load [Gbps]

Fra

ctio

n of

pac

kets

suc

cess

fully

tran

sfer

red

TOPOLOGY 4. Poisson constant traffic (type 1). 1 user each node

1 λ 2 λ 3 λ M/M/L/k (4 MR) M/M/L/k (∞ MR)

Figure 29. Throughput of topology four, one user for each node at low network loads.

In this case it is clear that an optimal proportion between network capacity and number of

FDLs is present: in fact the case with two channels on each link gives out a better throughput

with respect to the cases with one and three channels. The best solution can be found also

in this case using the optimization methods of chapter 4, as shown in Figure 30. A complete

description of the optimum topologies can be found in section C.3.

121

0 5 10 15 20 25 300.9

0.91

0.92

0.93

0.94

0.95

0.96

0.97

0.98

0.99

1

Total network load [Gbps]

Fra

ctio

n of

pac

kets

suc

cess

fully

tran

sfer

red

TOPOLOGY 5. Poisson constant traffic (type 1). 1 user each node

1 λ 2 λ 3 λ M/M/L/k (4 MR) M/M/L/k (∞ MR)

Figure 30. Throughput of topology five.

122

0 5 10 15 20 25 300

0.5

1

1.5

2

2.5

3

Total network load [Gbps]

Pac

kets

net

del

ay

TOPOLOGY 5. Poisson constant traffic (type 1). 1 user each node

1 λ 2 λ 3 λ M/M/L/k (4 MR) M/M/L/k (∞ MR)

Figure 31. Delay of topology five.

CHAPTER 6

CONCLUSIONS

The study of optical networks requires tools capable to handle the new characteristics of this

kind of architectures. In this thesis a new simulator was developed to measure the performances

of an optical network: it is topology independent, and a large variety of different networks can

be actively studied using it as a flexible and adaptable tool. The analysis of the simulated

network is not restricted to a global measure: the data on the traffic and other important

quantities referred to each link are collected, to allow an easy diagnosis of all the elements of

the network.

In this thesis a model for the next generation optical networks was also developed, exploit-

ing the potential of the classical queueing theory: in this way the problem of the optimum

assignment of the available resources among the different elements of a network is faced and

can be solved through a heuristic approach. The proposed solution does not guarantee to find

the general optimum solution for a given topology, nor the model on which it is based is per-

fectly equivalent to the adopted node architecture. Nevertheless, the topologies calculated in

this way have the best performances with respect to other empirical setups: thus the proposed

optimization method can be adopted to design an optical network with fairly good results.

123

APPENDICES

124

125

Appendix A

OPTICAL COMPONENTS REPRESENTATION

The optical components drawn in the figures of this work are grouped in this appendix, to

give a general view and a quick reference in case of doubts. All the schematic representations

of the optical components are reported in Figure 32 and Figure 33.

126

Appendix A (Continued)

Multiplexer Demultiplexer Opticalcrossconnect

Wavelength converter

Tunablewavelength converter

SOAgate

Filter Tunable filter

Fiber delay line

Figure 32. Optical component representation, part one.

127

Appendix A (Continued)

3R

Coupler Isolator

Laser 3Rregenerator

Circulator Fiber Bragg grating

2x2coupler

Passivestar coupler

Figure 33. Optical component representation, part two.

128

Appendix B

NETWORK DESCRIPTION FILE

This appendix contains an example of the file, written in RC language, used to describe

topology number one (see Figure 18); in particular, the file regarding the optimum topology

derived from the application of the algorithm of chapter 4 is reported, in the case of total

network load equal to 14 Gbps.

As it can be seen in Table I, it can be divided in four parts: the first one contains the

default parameters that will be applied to the network elements; the connections forming the

physical network topology are described in the second part; the third and the fourth parts

contain information on the users and the links, respectively.

The default parameters grouped at the beginning of the file include:

• the accuracy of the simulation is equal to 0.2%;

• the highest link capacity of the network is equal to 4 Gbps (this is the so called ’reference

capacity’, see section 3.1.4 for details);

• the speed of the signal in the optical fiber is equal to 2 · 108 m/s;

• the users produce packets with Poisson interarrival time: they belong to type one de-

scribed in section 3.2.2;

129

Appendix B (Continued)

• the ’timescale’ parameter is equal to four: this implies that the default link capacity is

equal to:

default link capacity =reference capacity

default link timescale= 1 Gbps (B.1)

• the default length for all the links is 10 Km;

The description of the connections between the nodes (and from the nodes to the users) is

reported in the second part of the file; for example from this part it is possible to see that the

link connecting node number one and number two provides four wavelength channels, and that

the network counts eight users, one for each boundary node. The links from/to the users have

only one wavelength channel, as explained in section 3.2.1.

The connections from the users to the nodes are declared in the third part. The last part of

the RC file declares the capacity of the links that differs from the default capacity (calculated

in Equation B.1); in this case the timescale of all the links from/to the users is declared equal

to one: so they have a capacity equal to four Gbps.

130

Appendix B (Continued)

TABLE I

LISTING OF RC FILE

/*

** File obtained with total traffic equal to 14000.000 Mbps

** OPTIMUM number of lambdas.

**

** Author: Walter Picco, 2001.

** RC File.

** Links: 10Km, 1Gbps.

** 8 users (1 for each boundary node) with links of 4 Gbps.

*/

/*

** DEFAULT Parameters Section

*/

/* Typical Statistical Parameters */

default accuracy = 0.002,

num_observation = 10000000,

min_trans_len = 100,

trans_batch_size = 20,

trans_seq_len = 3,

trans_accuracy = 0.5,

stat_batch_size = 1000;

/* Mandatory Global Network parameters */

default capacity=4000.0, /* This is the capacity of the link */

/* with the highest capacity in the */

/* network the so called ’reference */

/* capacity’. */

speed_in_fiber=2.0; /* Signal speed in optical fibers */

/* User Default Parameter */

131

Appendix B (Continued)

TABLE I (CONTINUE)

LISTING OF RC FILE

default user_buffer = 10000;

default user_type = poisson_const;/* POISSON traffic */

default u1_mess_length = 1;

/* Link Default Parameter */

default link_timescale = 4, /* The node/node links are 1 Gbps */

link_length = 10.; /* All links are 10 km long */

default link_buffer = 1;

/*

** Network description

*/

node node_1 with

connected_to node_5 thru n1_n5;

connected_to node_5 thru n1_n5l1;

connected_to node_5 thru n1_n5l2;

connected_to node_6 thru n1_n6;

connected_to node_6 thru n1_n6l1;

connected_to node_6 thru n1_n6l2;

connected_to node_2 thru n1_n2;

connected_to node_2 thru n1_n2l1;

connected_to node_2 thru n1_n2l2;

connected_to node_2 thru n1_n2l3;

connected_to node_4 thru n1_n4;

connected_to node_4 thru n1_n4l1;

connected_to node_4 thru n1_n4l2;

endwith;

node node_2 with

connected_to node_1 thru n2_n1;

connected_to node_1 thru n2_n1l1;

connected_to node_1 thru n2_n1l2;

connected_to node_1 thru n2_n1l3;

connected_to node_7 thru n2_n7;

132

Appendix B (Continued)

TABLE I (CONTINUE)

LISTING OF RC FILE

connected_to node_7 thru n2_n7l1;

connected_to node_7 thru n2_n7l2;

connected_to node_8 thru n2_n8;

connected_to node_8 thru n2_n8l1;

connected_to node_8 thru n2_n8l2;

connected_to node_3 thru n2_n3;

connected_to node_3 thru n2_n3l1;

connected_to node_3 thru n2_n3l2;

endwith;

node node_3 with

connected_to node_4 thru n3_n4;

connected_to node_4 thru n3_n4l1;

connected_to node_2 thru n3_n2;

connected_to node_2 thru n3_n2l1;

connected_to node_2 thru n3_n2l2;

connected_to node_9 thru n3_n9;

connected_to node_9 thru n3_n9l1;

connected_to node_9 thru n3_n9l2;

connected_to node_10 thru n3_n10;

connected_to node_10 thru n3_n10l1;

connected_to node_10 thru n3_n10l2;

endwith;

node node_4 with

connected_to node_12 thru n4_n12;

connected_to node_12 thru n4_n12l1;

connected_to node_12 thru n4_n12l2;

connected_to node_1 thru n4_n1;

connected_to node_1 thru n4_n1l1;

connected_to node_1 thru n4_n1l2;

connected_to node_3 thru n4_n3;

connected_to node_3 thru n4_n3l1;

connected_to node_11 thru n4_n11;

connected_to node_11 thru n4_n11l1;

connected_to node_11 thru n4_n11l2;

133

Appendix B (Continued)

TABLE I (CONTINUE)

LISTING OF RC FILE

endwith;

node node_5 with

connected_to node_1 thru n5_n1;

connected_to node_1 thru n5_n1l1;

connected_to node_1 thru n5_n1l2;

used_by user5 thru user5_da_n5;

endwith;

node node_6 with

connected_to node_1 thru n6_n1;

connected_to node_1 thru n6_n1l1;

connected_to node_1 thru n6_n1l2;

used_by user6 thru user6_da_n6;

endwith;

node node_7 with

connected_to node_2 thru n7_n2;

connected_to node_2 thru n7_n2l1;

connected_to node_2 thru n7_n2l2;

used_by user7 thru user7_da_n7;

endwith;

node node_8 with

connected_to node_2 thru n8_n2;

connected_to node_2 thru n8_n2l1;

connected_to node_2 thru n8_n2l2;

used_by user8 thru user8_da_n8;

endwith;

node node_9 with

connected_to node_3 thru n9_n3;

connected_to node_3 thru n9_n3l1;

connected_to node_3 thru n9_n3l2;

used_by user9 thru user9_da_n9;

endwith;

134

Appendix B (Continued)

TABLE I (CONTINUE)

LISTING OF RC FILE

node node_10 with

connected_to node_3 thru n10_n3;

connected_to node_3 thru n10_n3l1;

connected_to node_3 thru n10_n3l2;

used_by user10 thru user10_da_n10;

endwith;

node node_11 with

connected_to node_4 thru n11_n4;

connected_to node_4 thru n11_n4l1;

connected_to node_4 thru n11_n4l2;

used_by user11 thru user11_da_n11;

endwith;

node node_12 with

connected_to node_4 thru n12_n4;

connected_to node_4 thru n12_n4l1;

connected_to node_4 thru n12_n4l2;

used_by user12 thru user12_da_n12;

endwith;

/*

** User Definition

*/

user user5 with

connected_to node_5 thru user5_a_n5;

endwith;

user user6 with

connected_to node_6 thru user6_a_n6;

endwith;

user user7 with

connected_to node_7 thru user7_a_n7;

135

Appendix B (Continued)

TABLE I (CONTINUE)

LISTING OF RC FILE

endwith;

user user8 with

connected_to node_8 thru user8_a_n8;

endwith;

user user9 with

connected_to node_9 thru user9_a_n9;

endwith;

user user10 with

connected_to node_10 thru user10_a_n10;

endwith;

user user11 with

connected_to node_11 thru user11_a_n11;

endwith;

user user12 with

connected_to node_12 thru user12_a_n12;

endwith;

/*

** Links user/node and node/user

*/

link user5_da_n5 with

parameter link_timescale = 1;

endwith;

link user5_a_n5 with

parameter link_timescale = 1;

endwith;

link user6_da_n6 with

parameter link_timescale = 1;

136

Appendix B (Continued)

TABLE I (CONTINUE)

LISTING OF RC FILE

endwith;

link user6_a_n6 with

parameter link_timescale = 1;

endwith;

link user7_da_n7 with

parameter link_timescale = 1;

endwith;

link user7_a_n7 with

parameter link_timescale = 1;

endwith;

link user8_da_n8 with

parameter link_timescale = 1;

endwith;

link user8_a_n8 with

parameter link_timescale = 1;

endwith;

link user9_da_n9 with

parameter link_timescale = 1;

endwith;

link user9_a_n9 with

parameter link_timescale = 1;

endwith;

link user10_da_n10 with

parameter link_timescale = 1;

endwith;

link user10_a_n10 with

parameter link_timescale = 1;

endwith;

link user11_da_n11 with

parameter link_timescale = 1;

endwith;

137

Appendix B (Continued)

TABLE I (CONTINUE)

LISTING OF RC FILE

link user11_a_n11 with

parameter link_timescale = 1;

endwith;

link user12_da_n12 with

parameter link_timescale = 1;

endwith;

link user12_a_n12 with

parameter link_timescale = 1;

endwith;

/* END of the RC File */

138

Appendix C

OPTIMIZATION RESULTS

The data obtained applying to different topologies the optimization algorithm described in

chapter 4 are reported here. The paths of the cells from the source to the destination nodes

are determined automatically by the routing algorithm of the program; the inputs required to

obtain these results are limited to the RC file with the topology description, the total network

load and the number of input/output ports for each node.

C.1 Topology number one

In the case of the general backbone topology (depicted in Figure 18) the number of in-

put/output ports is equal to 16, each with capacity equal to 1 Gbps. The number of ports

dedicated to add capacity into the network and the number of FDLs for each node are reported

in Table II; the ports that are not used to solve contentions are indicated as ’channel ports’ in

the tables for sake of brevity. The number of channels and the load (in packets/sec) of each

link are reported in Table III.

As it can be seen from the tables, the topologies calculated with the optimization algorithm

are slightly different changing the network load: this is due to the particular method adopted

to calculate the initial topology in the algorithm, involving the traffic matrix (see section 4.4.2).

139

Appendix C (Continued)

TABLE II

TOPOLOGY ONE: NUMBER OF CHANNEL PORTS AND NUMBER OF FDLS OF THENODES FOR DIFFERENT NETWORK LOADS.

Load = 10 Gbps Load = 14 Gbps Load = 18 Gbps

Node Channel Channel Channelnumber ports FDLs ports FDLs ports FDLs

1 12 4 13 3 13 35 3 13 3 13 3 136 3 13 3 13 3 132 12 4 13 3 13 34 12 4 11 5 12 47 3 13 3 13 3 138 3 13 3 13 3 133 12 4 11 5 12 49 3 13 3 13 3 1310 3 13 3 13 3 1312 3 13 3 13 3 1311 3 13 3 13 3 13

140

Appendix C (Continued)

TABLE III

TOPOLOGY ONE: NUMBER OF CHANNELS ON EACH LINK FOR DIFFERENTNETWORK LOADS.

Load = 10 Gbps Load = 14 Gbps Load = 18 Gbps

Node Node Traffic Number of Traffic Number of Traffic Number offrom to [pkts/s] channels [pkts/s] channels [pkts/s] channels

1 5 31250.000 3 43750.000 3 56250.000 31 6 31250.000 3 43750.000 3 56250.000 31 2 44642.857 3 75000.000 4 80357.143 41 4 35714.286 3 50000.000 3 80357.143 32 1 49107.143 3 75000.000 4 96428.571 42 7 31250.000 3 43750.000 3 56250.000 32 8 31250.000 3 43750.000 3 56250.000 32 3 40178.571 3 62500.000 3 64285.714 33 4 26785.714 3 31250.000 2 40178.571 33 2 44642.857 3 62500.000 3 80357.143 33 9 31250.000 3 43750.000 3 56250.000 33 10 31250.000 3 43750.000 3 56250.000 34 12 31250.000 3 43750.000 3 56250.000 34 1 31250.000 3 50000.000 3 64285.714 34 3 31250.000 3 31250.000 2 56250.000 34 11 31250.000 3 43750.000 3 56250.000 35 1 31250.000 3 43750.000 3 56250.000 36 1 31250.000 3 43750.000 3 56250.000 37 2 31250.000 3 43750.000 3 56250.000 38 2 31250.000 3 43750.000 3 56250.000 39 3 31250.000 3 43750.000 3 56250.000 310 3 31250.000 3 43750.000 3 56250.000 311 4 31250.000 3 43750.000 3 56250.000 312 4 31250.000 3 43750.000 3 56250.000 3

141

Appendix C (Continued)

C.2 Topology number four

The results of the optimization algorithm are reported also for the case of topology number

four (see Figure 19). The number of FDLs and channel ports are listed in Table IV: each

node has 16 ports in total, each having a capacity equal to 1 Gbps. The number of wavelength

channels for each link together with the traffic load are reported in Table V: also in this case

the optimum topology changes as a function of the total network load.

C.3 Topology number five

The final tables report the results of the optimization algorithm applied on topology number

five (depicted in Figure 20). In this case the nodes have 24 input/output ports, and the capacity

of any single channel is equal to 1 Gbps. The number of FDLs and channel ports for each node

are reported in Table VI, while Table VII contains the number of wavelength channels and the

traffic for each link in the case of different network loads.

142

Appendix C (Continued)

TABLE IV

TOPOLOGY FOUR: NUMBER OF CHANNEL PORTS AND NUMBER OF FDLS OFTHE NODES FOR DIFFERENT NETWORK LOADS.

Load = 4 Gbps Load = 20 Gbps Load = 40 Gbps

Node Channel FDLs Channel FDLs Channel FDLsnumber ports ports ports

1 4 12 6 10 6 102 6 10 9 7 8 85 6 10 9 7 9 73 7 9 8 8 9 74 6 10 8 8 8 87 8 8 10 6 9 76 9 7 12 4 13 38 4 12 6 10 6 1010 10 6 12 4 11 512 11 5 13 3 11 59 6 10 9 7 8 814 4 12 5 11 5 1111 6 10 9 7 6 1015 11 5 12 4 12 416 5 11 7 9 6 1013 5 11 6 10 7 920 9 7 9 7 12 421 8 8 9 7 13 318 11 5 11 5 11 519 10 6 10 6 11 517 4 12 4 12 4 1223 6 10 6 10 6 1022 5 11 6 10 6 1024 7 9 9 7 8 826 8 8 8 8 11 527 6 10 6 10 9 728 4 12 4 12 6 1025 4 12 5 11 5 11

143

Appendix C (Continued)

TABLE V

TOPOLOGY FOUR: NUMBER OF CHANNELS ON EACH LINK FOR DIFFERENTNETWORK LOADS.

Load = 4 Gbps Load = 20 Gbps Load = 40 Gbps

Node Node Traffic Number of Traffic Number of Traffic Number offrom to [pkts/s] channels [pkts/s] channels [pkts/s] channels

1 2 2513.228 2 23148.148 3 38359.788 31 5 3042.328 2 22486.772 3 44973.545 32 1 1984.127 2 22486.772 3 35714.286 32 3 2910.053 2 17857.143 3 35714.286 32 4 1851.852 2 14550.265 3 22486.772 23 2 2513.228 2 15873.016 3 30423.280 33 4 1851.852 2 7936.508 2 30423.280 33 7 5423.280 3 23809.524 3 55555.556 34 2 1719.577 2 15873.016 3 25132.275 24 3 1984.127 2 6613.757 2 25132.275 34 6 4365.079 2 21825.397 3 51587.302 35 1 3571.429 2 23148.148 3 47619.048 35 6 5555.556 2 21825.397 3 59523.810 35 8 1851.852 2 25793.651 3 34391.534 36 4 4365.079 2 21825.397 3 48941.799 36 5 5026.455 2 20502.646 3 43650.794 36 7 4100.529 2 23148.148 3 52910.053 36 10 9126.984 3 37698.413 3 105820.106 47 3 4894.180 3 23148.148 3 55555.556 37 6 3439.153 2 21825.397 3 43650.794 37 12 9391.534 3 44312.169 4 101851.852 38 5 2910.053 2 27777.778 3 52910.053 38 9 2910.053 2 27116.402 3 30423.280 39 8 3968.254 2 29100.529 3 48941.799 39 10 3571.429 2 23809.524 3 33068.783 29 14 3571.429 2 25793.651 3 35714.286 310 6 9259.259 3 37698.413 3 96560.847 410 9 3968.254 2 24470.899 3 44973.545 210 11 3703.704 2 25793.651 3 41005.291 210 15 8465.608 3 32407.407 3 92592.593 311 10 4365.079 2 26455.026 3 42328.042 211 12 3835.979 2 24470.899 3 42328.042 2

144

Appendix C (Continued)

TABLE V (CONTINUE)

TOPOLOGY FOUR: NUMBER OF CHANNELS ON EACH LINK FOR DIFFERENTNETWORK LOADS.

Load = 4 Gbps Load = 20 Gbps Load = 40 Gbps

Node Node Traffic Number of Traffic Number of Traffic Number offrom to [pkts/s] channels [pkts/s] channels [pkts/s] channels

11 16 2910.053 2 25132.275 3 35714.286 212 7 8201.058 3 42328.042 4 92592.593 312 11 4100.529 2 25132.275 3 42328.042 212 13 5952.381 3 27116.402 3 75396.825 312 20 7010.582 3 29761.905 3 58201.058 313 12 5687.831 3 30423.280 3 71428.571 313 21 4497.354 2 21825.397 3 51587.302 414 9 4232.804 2 27116.402 3 42328.042 314 15 3703.704 2 25132.275 2 34391.534 215 10 8333.333 3 32407.407 3 93915.344 315 14 4365.079 2 26455.026 2 41005.291 215 16 1190.476 1 19179.894 2 25132.275 215 18 9391.534 3 33730.159 3 99206.349 315 19 3835.979 2 24470.899 2 31746.032 216 11 3306.878 2 25132.275 3 37037.037 216 15 1984.127 1 18518.519 2 25132.275 216 19 1190.476 2 24470.899 2 25132.275 217 18 4365.079 2 21164.021 2 47619.048 217 23 1455.026 2 11243.386 2 18518.519 218 15 8994.709 3 35714.286 3 103174.603 318 17 3835.979 2 19179.894 2 39682.540 218 19 6216.931 2 27116.402 2 46296.296 218 22 3835.979 2 19841.270 2 43650.794 218 24 3571.429 2 19841.270 2 35714.286 219 15 3968.254 2 24470.899 2 35714.286 219 16 2380.952 2 23809.524 2 26455.026 219 18 4100.529 2 27777.778 2 42328.042 219 20 4629.630 2 23148.148 2 41005.291 319 26 4232.804 2 22486.772 2 30423.280 220 12 6349.206 3 25132.275 3 52910.053 320 19 4497.354 2 23148.148 2 47619.048 320 21 3439.153 2 21164.021 2 26455.026 3

145

Appendix C (Continued)

TABLE V (CONTINUE)

TOPOLOGY FOUR: NUMBER OF CHANNELS ON EACH LINK FOR DIFFERENTNETWORK LOADS.

Load = 4 Gbps Load = 20 Gbps Load = 40 Gbps

Node Node Traffic Number of Traffic Number of Traffic Number offrom to [pkts/s] channels [pkts/s] channels [pkts/s] channels

20 26 3571.429 2 19841.270 2 34391.534 321 13 4232.804 2 25132.275 3 47619.048 421 20 3703.704 2 19179.894 2 34391.534 321 27 1851.852 2 12566.138 2 22486.772 321 28 2116.402 2 10582.011 2 23809.524 322 18 4232.804 2 19841.270 2 51587.302 222 23 1322.751 2 7936.508 2 19841.270 222 24 661.376 1 8597.884 2 7936.508 223 17 1984.127 2 13227.513 2 26455.026 223 22 1719.577 2 7936.508 2 15873.016 223 24 793.651 2 7275.132 2 17195.767 224 18 4365.079 2 19179.894 2 27777.778 224 22 661.376 1 8597.884 2 19841.270 224 23 1719.577 2 9259.259 2 21164.021 224 25 2380.952 2 22486.772 3 26455.026 225 24 4100.529 2 23809.524 3 34391.534 225 26 2380.952 2 25132.275 2 31746.032 326 19 3571.429 2 22486.772 2 25132.275 226 20 2513.228 2 17195.767 2 27777.778 326 25 4100.529 2 26455.026 2 39682.540 326 27 3306.878 2 20502.646 2 30423.280 327 21 1587.302 2 13227.513 2 25132.275 327 26 3306.878 2 19179.894 2 26455.026 327 28 1455.026 2 7275.132 2 18518.519 328 21 2380.952 2 11243.386 2 25132.275 328 27 1190.476 2 6613.757 2 17195.767 3

146

Appendix C (Continued)

TABLE VI

TOPOLOGY FIVE: NUMBER OF CHANNEL PORTS AND NUMBER OF FDLS OF THENODES FOR DIFFERENT NETWORK LOADS.

Load = 10 Gbps Load = 20 Gbps Load = 30 Gbps

Node Channel FDLs Channel FDLs Channel FDLsnumber ports ports ports

1 4 20 4 20 6 182 7 17 6 18 9 153 12 12 12 12 17 75 20 4 16 8 18 64 6 18 6 18 7 177 8 16 8 16 9 158 14 10 14 10 15 96 5 19 4 20 5 199 12 12 10 14 10 1410 9 15 8 16 8 1611 5 19 4 20 4 20

147

Appendix C (Continued)

TABLE VII

TOPOLOGY FIVE: NUMBER OF CHANNELS ON EACH LINK FOR DIFFERENTNETWORK LOADS.

Load = 10 Gbps Load = 20 Gbps Load = 30 Gbps

Node Node Traffic Number of Traffic Number of Traffic Number offrom to [pkts/s] channels [pkts/s] channels [pkts/s] channels

1 2 9090.909 2 22727.273 2 34090.909 31 3 15909.091 2 27272.727 2 40909.091 32 1 9090.909 2 22727.273 2 34090.909 32 3 9090.909 2 27272.727 2 27272.727 32 5 15909.091 3 27272.727 2 54545.455 33 1 15909.091 2 27272.727 2 40909.091 33 2 9090.909 2 27272.727 2 34090.909 33 4 11363.636 2 27272.727 2 34090.909 33 5 11363.636 2 22727.273 2 27272.727 23 7 13636.364 2 22727.273 2 34090.909 33 8 13636.364 2 22727.273 2 34090.909 34 3 13636.364 2 27272.727 2 34090.909 34 5 4545.455 2 18181.818 2 27272.727 24 8 6818.182 2 22727.273 2 27272.727 25 2 15909.091 3 27272.727 2 47727.273 35 3 9090.909 2 22727.273 2 34090.909 25 4 4545.455 2 22727.273 2 27272.727 25 6 13636.364 3 27272.727 2 47727.273 35 7 6818.182 2 18181.818 2 27272.727 25 8 6818.182 2 13636.364 2 20454.545 25 9 13636.364 3 22727.273 2 40909.091 25 10 13636.364 3 22727.273 2 34090.909 26 5 13636.364 3 27272.727 2 47727.273 36 8 9090.909 2 22727.273 2 27272.727 27 3 13636.364 2 22727.273 2 34090.909 37 5 6818.182 2 18181.818 2 27272.727 27 8 4545.455 2 13636.364 2 27272.727 27 9 9090.909 2 22727.273 2 27272.727 28 3 13636.364 2 22727.273 2 34090.909 38 4 9090.909 2 18181.818 2 27272.727 28 5 6818.182 2 18181.818 2 27272.727 28 6 9090.909 2 22727.273 2 27272.727 2

148

Appendix C (Continued)

TABLE VII (CONTINUE)

TOPOLOGY FIVE: NUMBER OF CHANNELS ON EACH LINK FOR DIFFERENTNETWORK LOADS.

Load = 10 Gbps Load = 20 Gbps Load = 30 Gbps

Node Node Traffic Number of Traffic Number of Traffic Number offrom to [pkts/s] channels [pkts/s] channels [pkts/s] channels

8 7 4545.455 2 13636.364 2 20454.545 28 9 6818.182 2 18181.818 2 34090.909 28 10 11363.636 2 22727.273 2 27272.727 29 5 11363.636 3 22727.273 2 40909.091 29 7 9090.909 2 22727.273 2 34090.909 29 8 9090.909 2 22727.273 2 34090.909 29 10 2272.727 2 13636.364 2 20454.545 29 11 13636.364 3 22727.273 2 40909.091 210 5 13636.364 3 22727.273 2 27272.727 210 8 11363.636 2 18181.818 2 27272.727 210 9 4545.455 2 18181.818 2 27272.727 210 11 9090.909 2 22727.273 2 27272.727 211 9 11363.636 3 22727.273 2 40909.091 211 10 11363.636 2 22727.273 2 27272.727 2

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VITA

NAME: Walter Picco

EDUCATION: B.S. equivalent certificate, Electrical Engineering,

Politecnico di Torino, Turin, Italy, 2000.

M.S. Electrical Engineering, University of Illinois

at Chicago, Chicago, Illinois, 2002.

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