insituto tecnologico y de estudiossuperiores de monterrey

campus monterrey

division de tecnologias de la informacion y electronica

a performance comparison of contention resolution andresource allocation in slotted optical burst switched

vs. optical packet switched network scenarios

Proyecto de Fin de Carrera presentado como requisitoparcial para obtener el grado academico de:

ingeniero de telecomunicacion

por:

Ines Chavarri Burguete

mayo, 2011

Agradecimientos

A mi asesor, el Dr. Jorge Carlos Mex, por sus indispensables consejos, suapoyo y su ayuda en todo momento.

A Miguel Bautista Leon, por su trabajo en el CET que permitio eldesarrollo del mıo y por su colaboracion.

Al Dr. Gerardo Castanon, por su disponibilidad a la hora de revisar estedocumento.

A Ivan Razo-Zapata, por sus valiosos comentarios en el desarrollo delproyecto.

A Alberto Herrera, por su amabilidad y ayuda.

Al Instituto Tecnologico y de Estudios Superiores de Monterrey, por lasfacilidades provistas.

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List of Figures

2.1 OPS Network . . . . . . . . . . . . . . . . . . . . . . . . . . . 92.2 OBS Network . . . . . . . . . . . . . . . . . . . . . . . . . . . 102.3 OPS Architecture . . . . . . . . . . . . . . . . . . . . . . . . . 122.4 SOBS Architecture . . . . . . . . . . . . . . . . . . . . . . . . 14

3.1 European Optical Network . . . . . . . . . . . . . . . . . . . 17

4.1 SOBS performance for Poisson traffic . . . . . . . . . . . . . . 204.2 Switch OPS vs. Switch SOBS under Poisson traffic . . . . . . 214.3 Switch OPS vs. Switch SOBS under exponential traffic . . . . 224.4 Switch OPS vs. Switch SOBS under Pareto traffic . . . . . . 234.5 Fibers under Poisson distribution with a traffic load of 0.8 . . 244.6 FDLs under Poisson distribution with a traffic load of 0.8 . . 244.7 Fibers under exponential distribution with a traffic load of 0.8 254.8 FDLs under exponential distribution with a traffic load of 0.8 254.9 Fibers under Pareto distribution with a traffic load of 0.5 and

H = 0.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 264.10 FDLs under Pareto distribution with a traffic load of 0.5 and

H = 0.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

1

Contents

1 Introduction 41.1 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51.2 Problem Approach . . . . . . . . . . . . . . . . . . . . . . . . 51.3 Hypothesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61.4 Justification . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2 Theoretical background 82.1 OPS, OBS and SOBS theoretical background . . . . . . . . . 8

2.1.1 OPS . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82.1.2 OBS . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92.1.3 SOBS . . . . . . . . . . . . . . . . . . . . . . . . . . . 92.1.4 SOBS previous work . . . . . . . . . . . . . . . . . . . 11

2.2 OPS switch model in which SOBS is based . . . . . . . . . . 112.2.1 Opsim . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.3 SOBS Switch design . . . . . . . . . . . . . . . . . . . . . . . 122.4 Traffic models . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

3 Methodology 163.1 Description of the burst aggregator . . . . . . . . . . . . . . . 163.2 Basis for OPS and SOBS comparison . . . . . . . . . . . . . . 18

4 Simulations and Results 194.1 SOBS switch . . . . . . . . . . . . . . . . . . . . . . . . . . . 194.2 OPS switch vs. SOBS switch . . . . . . . . . . . . . . . . . . 20

4.2.1 Poisson distribution . . . . . . . . . . . . . . . . . . . 204.2.2 Exponential distribution . . . . . . . . . . . . . . . . . 214.2.3 Pareto distribution . . . . . . . . . . . . . . . . . . . . 21

4.3 OPS network vs. SOBS network . . . . . . . . . . . . . . . . 22

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5 Conclusions and future work 285.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285.2 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

A Acronyms 31

3

Chapter 1

Introduction

The use of Internet is growing together with the amount and size of the datashared through it. Emerging dynamic high-bandwidth network applicationsrequire the implementation of networks capable of supporting the necessarybandwidth for them. The emergence of optical communication technologyhas led to the extended implementation of Optical fiber networks that arecapable of meeting this need. Although early deployments involved opticalfiber links and optical-electronic-optical switches in order for the informationto be processed electronically, late deployments started to make use of all-optical switching technology, to take advantage of the possibility of avoidingthe costly conversion to electronics at intermediate nodes.

Firstly deployed optical networks primarily employed optical circuit switch-ing [1]. In these networks, a lightpath is established between source and des-tination node before the data is transmitted and therefore it requires round-trip signalling to reserve the necessary resources. They are well suited forpersistent high-bandwidth traffic that does not vary much over time; how-ever it does not suit that well Internet applications dynamic bursty traffic.Circuits tend to be static and provide a fixed amount of bandwidth, so theydo not make efficient use of the resources. Furthermore, additional ineffi-ciency and overhead are produced as a result of the transmission time forthe data transfer being smaller than to the round trip propagation delayrequired by the signalling in many on-demand data transfers.

Because of these disadvantages, much research has been conducted toovercome them towards the development of Optical Packet Switching (OPS)and Optical Burst Switching (OBS). Both proposals attempt to use re-sources only when the data is being transmitted, thereby providing sta-tistical multiplexing and a higher degree of utilization than optical circuit

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switching.In recent years, Slotted Optical Burst Switching (SOBS) has been pro-

posed as an alternative to OPS and OBS joining some of the advantages ofboth of them. The simplicity of handling synchronized information at nodesjoins the reduction of overhead small units of data would present.

At the same time, in the traditional traffic model, packets and interar-rival rates are often assumed to be a Poisson process, due to the simplicityof its mathematical model and its low correlation between its interarrivaltimes, which translates in the process having low memory [2]. In the be-ginning of the 90’s additional traffic studies were reported which showed apacket interarrival distribution different from exponential [3], and this trafficbehavior is better modeled using self-similar probability distributions. Be-cause of this, further studies are required to analyze the performance of allthe proposed models under this conditions which reflect in a more reliableway the true behavior of real traffic.

1.1 Objectives

• Implement a SOBS switch simulator based on an OPS switch

• Compare the performance of OPS and SOBS switches in terms ofpacket loss probabilities under similar conditions of traffic for threedifferent traffic models: Poisson, Exponential and Pareto

• Compare the performance of the European optical network based oneach type of switch under different traffic models according to the costsassociated to the utilization of resources

1.2 Problem Approach

The fast growth of Telecommunication services has increased the require-ments of bandwidth globally. Optical networks seem to be a good approachto solve this problem. However, despite their numerous advantages, theystill need the development electronics have reached throughout the years.Difficulties like ineffective and expensive optical memories make it neces-sary to find new approaches to networking and the behavior of the nodesto avoid blocking situations. Therefore past and renewed switching modelsneed to be reviewed and applied to the optical domain, in order to mini-mize information loss, administer the network and address future quality ofservice Quality of Service (QoS) requirements.

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The models proposed up to date show some drawbacks which need to beaddressed. OPS has a large overhead associated to short data units whichaffects its efficiency. Furthermore, optical converters are not as developed aselectrical ones requiring an stabilization time, so the use of Total Wavelengthconversions (TWCs) for each packet might not be that easy to achieve.

At the same time, OBS asynchronous character causes inefficiencies too,as when a burst is only partially blocked, the whole burst must be droppedthus increasing the packet loss rate. In addition to this, packet delay mightbe critical for some applications and OBS increases it due to burst aggrega-tion time.

1.3 Hypothesis

SOBS might be a good alternative to the already proposed OPS and OBSmodels. It has not been plenty studied yet and it might benefit from lowerrequirements for the TWCs and solve packet header overhead inefficiency,yet maintaining the advantages derived from synchronicity, thus postulatingas a valid solution.

1.4 Justification

An increasing load of traffic is being shared through internet as people uti-lization rises and so does the size of the data transmitted. Optical fibernetworks are necessary to provide with the demanded bandwidth as tra-ditional electronic networks no longer can meet the growing requirements.And if all the potential is intended to be taken advantage of, all-optical net-works can offer a less costly alternative than optical fiber links connected toelectronic switches.

Still, this kind of networks is not by far fully developed. Switchingmodels are being reviewed in order to provide the most suitable solution tothe advantages and limitations optical domain offers. First circuit switchingand then OPS and OBS were proposed, and currently further research aboutthe last two is being held. Additionally SOBS was proposed as a way to tryto get together the advantages of both OPS and SOBS. Finding the bestpossible solution for optical switching is critical in order to exploit all thepossibilities offered by this technology. However, the effectiveness of thissolution when compared with the others is still to be proved so that it canreally be considered. Simulating its behavior under different conditions willhelp to know the steps to take in order to optimize its performance. In

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addition, hidden considerations which must be taken care of will be easierto analyze, keeping always in mind the real deployment of this networks andthe drawbacks which might not in the beginning be so evident.

Furthermore, the model of traffic used to test the nodes (and obviouslythe networks) will also be critical in order to accept the results of theirperformance. This is, the broader the study of the technology the better.Including self-similar traffic in the research guarantees nowadays a morerealistic assessment of the real capacity of the solution.

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Chapter 2

Theoretical background

2.1 OPS, OBS and SOBS theoretical background

As mentioned above, the first approach to optical domain only includedoptical fiber links but kept making use of electronic switches, so there wasa waste of the capacity due to the optical-electronic-optical conversions andthe decrease in the speed working in the electronic domain implies.

The first approach (optical circuit switching) was already discussed. Itscharacteristics made it unsuitable for dynamic applications such as the onesdominating the internet nowadays. As a result, much work has been con-ducted toward the development of OPS and OBS networks in order to avoidthe inefficient use of the resources [1].

2.1.1 OPS

An OPS consists of a packet-by-packet switching in the optical domain with-out conversion to electronics at intermediate nodes as shown in Figure 2.1.Each packet contains the control information, thus offering the most efficientutilization of the bandwidth. Nevertheless, the success of OPS relies heavilyon device technology and properly designed architectures for providing aset of basic functionalities required for switching packets in the optical do-main. These functionalities include packet synchronization, packet headerprocessing, switching and contention resolution. Furthermore, it would needto overcome a considerable overhead as each packet is processed individually.

8

Figure 2.1: OPS Network

2.1.2 OBS

As a way to partially avoid the overhead related to packet processing, OBShas been proposed. The underlying idea is collecting data into bursts andswitching them through the network optically as can be seen in Figure 2.2.The resources are reserved out of band and ahead of the information, re-ducing signalling overhead and reducing buffering of data at intermediatenodes. On the other hand the process of creating bursts translates in adelay in individual packets that must be taken into account in certain appli-cations. The one way reservation mechanism reduces the signalling overheadcompared to optical circuit switching.

2.1.3 SOBS

Trying to obtain the benefits from OBS while reducing the data loss, SlottedOBS SOBS was introduced. It is also known as Synchronous OPtical BurstSwitching (SyncOBS) by other authors, but the concept is essentially thesame.

Traditional OBS protocols assume size-varied bursts arriving asynchronously[4]. When two or more bursts compete for an outgoing wavelength and there

9

Figure 2.2: OBS Network

are no means of buffering optical data one of them must be blocked. Andgiven the asynchronous character of the bursts, a situation might happenwhen a burst is only partially blocked, but still the entire content of the burstmust be dropped. Obviously this leads to an inefficient resource utilizationof outgoing wavelengths.

Alternatively, if optical buffering resources are available at intermediatenodes and two arriving bursts compete for an outgoing wavelength, the firstone will get it while the second one is stored. However, the most commonoptical buffering, Fiber Delay Lines (FDLs), consist on a simple optical fiberattached to the node, thus providing with a fixed duration determined by itslength. Even if the first burst finishes quickly, the second burst is delayedby the FDLs entire duration before it is forwarded out to the outgoingwavelength delaying the blocked burst more than necessary and inefficientlyutilizing the outgoing wavelength.

For these reasons SOBS is expected to achieve better resource resolutionby using synchronous timeslots which avoid the situations mentioned above.

It should be taken into account that this technique requires extra mech-anisms to provide synchronization at each node, and still suffers from anindividual packet delay worse than in OPS. This last option can also be

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addressed and partially solved by introducing the use of timers.

2.1.4 SOBS previous work

Although the concept of SOBS is relatively new there has already been a lotof research about it. It was confirmed that SOBS with fixed-length burstsshows smaller data loss than conventional OBS [5]. In another work relatedto QoS it was demonstrated the importance of SOBS, given that it showssimilar behavior than OBS with fewer wavelengths with a relatively hightraffic load [6]. Once the convenience of SOBS was assumed, plenty of workfocused on optimizing its implementation, for example by finding the bestsize of the timeslot [4].

2.2 OPS switch model in which SOBS is based

The whole SOBS simulator in which the present work is based relies on anOPS simulator used in [7]. And it is also used to make the comparisonsbetween both models of switching, OPS and SOBS. Thus, a description ofthe simulator is stated below.

2.2.1 Opsim

Opsim is a simulator of an optical packet switch whose architecture is shownin Figure 2.3.

Programmed in Programming language utilized (C++), it describes theoperation of a switch with the possibility of buffering the packets by meansof FDLs or converting them to other wavelengths by means of TWCs inorder to avoid packet loss in blocking situations.

Parameters as the simulation time, number of inlets, outlets, wave-lengths, FDL length, number of TWCs, or blocking solving strategy canbe chosen by the user. Specifically, two blocking solving strategies are of-fered. When a blocking situation occurs, wavelength conversion prior topacket buffering denominated Minimum packet buffering strategy (minBuff)can be the preferred method to solve it, or packet buffering prior to wave-length conversion denominated Minimum conversions strategy (minConv)can be chosen. When there are not enough resources available to relocatethe packet, it is dropped.

Originally it was designed to be fed (injection of all the inlets) in eachtime slot (i. e.: synchronously) with traffic uniformly distributed with acertain load the user could fix. The destinies of the packets were randomly

11

Figure 2.3: OPS Architecture

chosen. Statistics of all this happenings were collected so that the packetloss and its causes could later be analyzed.

Later on, different types of traffic were added to feed the OPS switch toevaluate its performance, including exponentially distributed, Poisson andPareto.

In addition, a network was also deployed in order to study the behavior ofthe switch in a real scenario. Optical networks such as the European werestudied with real parameters of distances, nodes and connections. Apartfrom this, all the parameters including number of fibers or number of wave-lengths and all of the ones related to the switches could be modified.

2.3 SOBS Switch design

As mentioned above, several research works have already been conducted onSOBS and its performance. The design of the switches varies greatly, but thevast majority of them evaluate the behavior of the switch to route the bursts,without taking care of their actual conformation. This is, that process wasomitted together with the influence it had on the whole performance. [[8]].

From all these, some were modeled using a simulation program were the

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parameters were introduced [8], [9]. Some others were based on simulatorspecially designed for the research[10]. Among the ones with their ownsimulator is the one on which the current work is based. In [4] describe theworking mode of a SOBS optical switch as:

Event : : a packet a r r i v e si f ( t imer t i s not s t a r t e d ){r e s t a r t t imer t ;}update b u f f e r \ s i z e ;i f ( $ b u f f e r \ s i z e \geq L$){schedu le the data burst to be sent out ;stop t imer t ;r e s e t b u f f e r \ s i z e ;}Event : : t imer t = Tschedu le the data burst to be sent out ;stop t imer t ;r e s e t b u f f e r \ s i z e ;

As we can see, two events can trigger the creation of a burst: the com-pletion of the timeslot length, given a maximum size for it; or the end of thetimer. The maximum size needs not to be completed exactly to match, butjust when an incoming packet arrives which would make exceed the lengthof the burst, then a burst is created and that packet will be the first in thenext burst. As for the timer, it is reset every time a burst is generated, andworks just as a way to avoid too long a delay in the packets. The resultingswitch architecture is shown in Figure 2.4.

2.4 Traffic models

The switch performance was evaluated against different types of traffic mod-els. First of all an interarrival time following Poisson distribution was understudy. According to this distribution, the probability of k occurrences duringa period of time is defined in Equation 2.1.

f(k, λ) =λxe−λ

k!(2.1)

λ is a positive real number, equal to the expected number of occurrencesduring the given interval and e is the base of the natural logarithm (e =2.71828...). The mean of this distribution is equal to λ, and so the traffic

13

Figure 2.4: SOBS Architecture

load can be controlled by the choice of this parameter given that traffic loadequals the number of events (f(lambda)) in a period of time.

Poisson distribution is the most commonly used in previous SOBS workas it is memory-less and consequently mathematically easy to describe. Itallows to be modeled analytically before actual simulations are done, thatbeing the reason why it is so extended its use.

In addition to this, exponentially distributed interarrival time was ex-plored, whose probability density function is given by Equation 2.2.

f(x, λ) =

{λe−λx x > 0

0 x < 0(2.2)

λ is the rate parameter and represents the number of occurrences in aperiod of time, e is the base of the natural logarithm (e = 2.71828...) and xis the time. As with Poisson distribution, the traffic load can be determinedby the λ parameter.

This distribution is typically used in queuing theory to describe the ser-vice times of agents in a system, and so can be applied to traffic generation.A Poisson distribution, it is memory less making it easy to model and there-fore to mathematically analyze.

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And finally, Pareto distributed traffic was under study. Recent workshows how self-similar traffic describes more realistically different types ofnetwork traffic: web services, IP traffic, video traffic, Metropolitan AreaNetwork (MAN) and Wide Area Network (WAN), among others. [11]. Someof the main factors that can produce the Long Range Dependence (LRD) ofdifferent types in the network traffic are: user’s behavior; data generation,data structure and its search; traffic aggregation; means of network control;control mechanisms based on feedback; network development [12].

Self-similar traffic has a fractal nature, this is, it shows traffic patternsrepeated through time. This is the traffic somehow works with memory; theprevious states of the network influence its present state.

Mathematically we have a random signal whose statisticians met therequirements defined in Equations 2.3, 2.4 and 2.5.

E[(X(t)] =E[X(αt)]

αH(2.3)

V ar[(X(t)] =V ar[X(αt)]

α2H(2.4)

Rx(t, s) =Rx(αt, αs)

α2H(2.5)

Therefore, the degree of self-similarity is defined by an only value, theH parameter or Hurst parameter, which is directly related to the form pa-rameter of a Pareto distribution by Equation 2.6:

H =3− a

2(2.6)

In the case of LRD H is between 0.5 and 1, which means values of abetween 2 and 1. H = 0.5 indicates lack of self-similarity, whereas for H=1the randomness disappears.

Therefore we can generate self-similar traffic form a Pareto distributionwith a ∈ (1, 2) [13]. It provides a method to analyze systems in a morerealistic scenario, given the fractal nature of real traffic.

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Chapter 3

Methodology

Based on the high-level language solution described above, a burst generatorwas developed. In order to make use of previous work, the next idea wasproposed: as the SOBS was characterized by making a synchronous burstout of asynchronous packets, it would be possible to treat burst as if theywere packets of a certain length inside the switch already implemented. Theonly necessary thing was to take into account the size of the elements as theywould determine this time the utilization and the overall loss probability.

3.1 Description of the burst aggregator

First of all interarrival times are generated according to the correspondingstatistical distribution (Poisson, Exponential, Uniform or Pareto) until thesum of them exceeds the time assigned to the timeslot. The number of pack-ets which will be generated in that timeslot will thus be equal to those whoseinterarrival times fit within the timeslot duration. Sizes can be assigned tothose packets.

For the bursts to be created the sizes of the packets are iterativelysummed up. If with the ones contained in a timeslot the maximum size(here the timeslot size) is not reached, no burst will be sent, which in thisconstruction of the switch consists of an empty object. When that numberis reached during a timeslot, the remaining packets are saved to be added inthe next timeslot, while a burst is generated with length equal to the totalaccumulated length of that burst. In order to reduce delays in case it takestoo long for a burst to be complete, there is a timer by which even if therequired length is not reached, the burst is generated anyway and sent atthe end of the timeslot (so timer is an integer number of timeslots).

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This burst generator was included in the switch simulator to generate thetraffic in each of the optical channels at the inlets as independent sources.The architecture can be observed in Figure 2.4.

Simulations were made to observe the behavior of the switch under dif-ferent traffic models and varying the resources available at the switch. Thepacket loss against the traffic load was the parameter chosen to representthe switch performance. The simulations were made for the different traf-fic distributions so that it was possible to compare them. The time-unitis defined as the shortest time in which a packet/burst might be created.For OPS it has the duration of a packet whereas for SOBS it is a timeslot(several packet units).

Later, the new SOBS switch was included as node of the European net-work shown in Figure 3.1 mentioned above among the previous work in orderto evaluate its performance when being part of a real network. Simulationsto determine the use of resources by the network were made, in terms offibers and delay lines to add in order to achieve lack of packet loss.

Figure 3.1: European Optical Network

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The results are shown in the next chapter.

3.2 Basis for OPS and SOBS comparison

The synchronicity of both switching modes makes the comparison easier,but still there are certain parameters such as the resources employed byeach of them which are not so evident to allocate. Here is the criteria usedto make the comparison as fair as possible.

Simulation time. The same total simulation time was employed for bothof them, but for SOBS it was grouped in timeslots. This is, OPS 108

time-units were compared with 107 timeslots of 10 time-units each inSOBS.

TWC. Although the length of the data processed by each of them is differ-ent, still the number of TWC assigned to both was the same, one peroptical outlet.

FDL. The number of FDLs assigned was based on the fiber length withouttaking into account other parameters such as processing complexity.This way, 1 FDL for SOBS has a length of 10 time-units (the size ofthe timeslot). On the other hand, for OPS the first FDL will havelength = 1 time-unit, the second one will have length 2 (so that unitscan wait for 2 unit-times inside), the third one will have length 3, etc.Following this logic, by using 4 FDL for OPS we will be employing1 + 2 + 3 + 4 = 10 time-units of resources. This way, the comparisonis made with 1 FDL for SOBS vs. 4 FDL for OPS.

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Chapter 4

Simulations and Results

The results shown in next sections will help to make a comparison betweenthe performance of OPS and SOBS. First the results of packet loss forthe SOBS switch alone are detailed. Then OPS and SOBS are simulatedunder the same conditions in a switch, and finally their performance in theEuropean network is evaluated.

4.1 SOBS switch

SOBS switch was tested for 108 simulation units, combined in 107 slots of10 time-units each. For a 4 x 4 switch with 4 wavelengths on each fiberdata was generated according to several distributions, conformed in burstsof length 10 and then introduced in the switch with a randomly chosendestiny. Once in the switch, conversion was the primary method to avoidblocking situations, minBuff, with 16 converters, followed by 0, 1, and 2optical buffers of length equal to burst duration in each case.

In Figure 4.1 we can see the influence of the switch size on its perfor-mance. The load varied from 0.1 to 0.9 and the number of TWCs wasinvariable, as many as optical outputs. Configurations of 4 x 4 and 8 x8 switch with 4 or 8 wavelengths were tested. The larger the number ofwavelengths, the lower the packet loss rate. So the more wavelengths areincluded in each fiber the more efficient will be the use of the resources tosolve blocking situations.

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Figure 4.1: SOBS performance for Poisson traffic

4.2 OPS switch vs. SOBS switch

For each kind of traffic a simulation was made. A 4 x 4 switch with 4wavelengths for 108 simulation units, combined in 107 slots of 10 units each.In case of SOBS the solving blocking situations mode was minBuff, whereasfor OPS was always minConv.

4.2.1 Poisson distribution

In Figure 4.2 the traffic loss rate against the load from 0.1 to 0.9 was testedfor Poisson distributed traffic with different amount of resources for OPSand SOBS switch. The use of FDLs is critical to achieve a low enoughpacket loss rate for both types of switches, although with no FDL availableSOBS has a better performance. SOBS can not achieve the performanceof OPS when FDLs are used, although the use of the converters would bemuch easier for long bursts than for single packets.

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Figure 4.2: Switch OPS vs. Switch SOBS under Poisson traffic

4.2.2 Exponential distribution

In Figure 4.3 the traffic loss rate against the load from 0.1 to 0.9 was testedfor exponentially distributed traffic with different amount of resources forOPS and SOBS switch. As with Poisson distributed traffic, SOBS cannotoutperform OPS when FDLs are included. Thus it seems under these modelof traffic OPS is a better option.

4.2.3 Pareto distribution

In Figure 4.4 the traffic loss rate against the H parameter from 0.5 to 0.9was tested for Pareto distributed traffic with different amount of resourcesfor OPS and SOBS switch.

The results show how with OPS high correlation (values of H close to1) translates into much higher packet loss rate, while SOBS tends to softenthis tendency making the loss rate similar for every value of H. This issupported in previous works such as [14] where it was demonstrated thatburst switching reduces the self-similarity of the traffic when compared topacket switching, therefore outperforming OPS. Still the burst aggregateprocess might be to blame of this effect as big bursts of packets generated

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Figure 4.3: Switch OPS vs. Switch SOBS under exponential traffic

in the sources can be reaching different destinies.

4.3 OPS network vs. SOBS network

In this case the resources required in a network are the characteristics tomeasure in order to make the comparison between OPS and SOBS.

In order to find out the number of FDLs and fibers per link at each node,a router and network dimensioning algorithm was applied as in [15]. Thedimensioning process occurs in two steps. During the first one, stabilization,every time a blocking situation occurs, the switch tries to solve it with theavailable converters and buffers. If blocking cannot be solved there is anincrement of the buffer depth by one unit. When the number of delay linesreaches the limit (in this case, 3), the number of fibers for that specific link isincreased. At the beginning the buffers depth is 0. This part of the processlasted for 40000 time-units.

After this, the actual simulation took place with the resources calculatedpreviously. With a duration of 1000 time-units, this part was useful to checkif the stabilization process had been long enough, as no more packets couldbe dropped, as was the case.

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Figure 4.4: Switch OPS vs. Switch SOBS under Pareto traffic

The topology used has 19 nodes. Each of them has an injection fiber atone of its inlets and an absorption fiber at one of its outlets. The rest of theinlets and outlets were connected to other nodes as shown in the topology ofEurope in Figure 3.1. The destinies of the packets were chosen randomly andthe routing was determined by an algorithm of shortest distance, solving theblocking situations by minBuff mode for both types of switch architecture.

Results of network dimensioning are shown next using the topology ofEurope in Figure 3.1 in terms of fibers per node and number of delay linesusing SOBS and OPS nodes as the ones described above.

For Poisson traffic with a traffic load of 0.8 the results are shown inFigure 4.5 for the fibers and Figure 4.6 for the delay lines. There we cansee how the number of fibers is not affected by the type of switch used, asboth of them require the use of 90, whereas there is an improvement in thenumber of FDLs required for SOBS, with only 87 versus 92 for OPS.

For Exponential traffic with a traffic load of 0.8 the results are shown inFigure 4.7 for the fibers and Figure 4.8 for the FDL. The need for resourcesis the same for the fibers, 89, and the number of FDLs is lower for OPS,just 75, which for exponentially distributed traffic outperforms SOBS, 78.

For Pareto distributed traffic with a traffic load of 0.5 and a Hurst pa-

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Figure 4.5: Fibers under Poisson distribution with a traffic load of 0.8

Figure 4.6: FDLs under Poisson distribution with a traffic load of 0.8

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Figure 4.7: Fibers under exponential distribution with a traffic load of 0.8

Figure 4.8: FDLs under exponential distribution with a traffic load of 0.8

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rameter of 0.8 the results are shown in Figure 4.9 for the fibers and Figure4.10 for the FDLs. Here is where we can find the bigger differences for OPSand SOBS. Although the number of FDLs is very close, 61 for SOBS and 63for OPS, the number of fibers required is remarkably smaller for SOBS,78vs 81 for OPS thus making it more efficient. Still these data needs to beconsidered carefully as mentioned above for the effect the burst creator hason self-similarity.

Figure 4.9: Fibers under Pareto distribution with a traffic load of 0.5 andH = 0.8

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Figure 4.10: FDLs under Pareto distribution with a traffic load of 0.5 andH = 0.8

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Chapter 5

Conclusions and future work

5.1 Conclusions

A study into the performance features of OPS and SOBS nodes and networkswas performed. Different traffic distributions were developed to allow thecomparison between those. Following, an analysis on the results is detailedin order to give an a solution to the problem statement provided in the firstchapter:

SOBS switch. The SOBS switch was implemented based on an OPS switchin order to study its behavior. It was shown how the packet loss ratediminishes as the number of wavelengths increases. So a bigger num-ber of wavelengths makes the use of the resources more efficient tosolve blocking situations.

OPS and SOBS switch comparison under different traffic distributions.Both switches were tested under different traffic conditions. For Pois-son and exponentially distributed traffic OPS switch proved to be thebest option, as SOBS switch was unable to outperform it. However,under Pareto distributed traffic, SOBS reduced the packet loss rate forhigh values of H parameter under the circumstances previously men-tioned for the burst creator. We can conclude that SOBS mitigatesthe effect self-similar traffic has on switch performance, thus makingthe dimensioning of switch resources more reliable and improving itsperformance under highly self-similar traffic. In more general termsSOBS would become the best candidate for real life optical networksgiven their demonstrated high self-similarity

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OPS and SOBS switch processing complexity comparison. Althoughthe switch performance comparison was made under similar resourceallocation, only their FDL length was taken into account. For theanalysis to be more accurate, processing costs would have to consideralso the fact of processing much bigger data units in each operation(burst vs. packets) making it more efficient or the convenience forcurrent TWCs to process bigger data units so that the stabilizationtime is not so critical a parameter.

OPS and SOBS network comparison. Again both architectures weretested under different traffic conditions. Under Poisson traffic the re-sources required were similar, with a slight improvement in the numberof FDLs for SOBS. Just the opposite as with exponential. Therefore,the implementation of SOBS switches under these traffic distributionsis not justified unless an extended study is made on SOBS effectiveresource consumption. However, under the same conditions of theswitch, self-similar traffic shows a better performance in SOBS net-work, which would make it a good candidate for real traffic patterns.

5.2 Future work

Further research on the introduced topics is to be done. Future work mustconsider:

Traffic distributions. Preservation of the traffic distribution patterns through-out the network and exploitation of the possibilities aggregating inbursts offers, especially for self-similar traffic has not been studied.

Packet delay. Thorough examination of packet delay in network scenariosfor SOBS is required. It can represent a critical parameter when QoSis required and a reason to reconsider the use of OPS as the packetsdo not suffer additional delay.

Quality of Service. The introduction of granted Quality of Service toboth OPS and SOBS represents a promising topic of study.

Strategies. Defined strategies should be applied to network scenarios ac-cording to the results obtained, intending to save resources by gettingadvantage of the network traffic distribution.

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Computing complexity. Additional research is required to model the ad-vantages/disadvantages of OPS, OBS and SOBS in terms of computingcomplexity at each node.

Energy consumption. As a consequence of the computing complexitymentioned above, energy requirements need to be determined as theyplay a critical role in implementation.

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Appendix A

Acronyms

C++ Programming language utilized

FDL Fiber Delay Line

LRD Long Range Dependence

MAN Metropolitan Area Network

minBuff Minimum packet buffering strategy

minConv Minimum conversions strategy

OBS Optical Burst Switching

OPS Optical Packet Switching

QoS Quality of Service

SOBS Slotted Optical Burst Switching

SyncOBS Synchronous OPtical Burst Switching

TWC Total Wavelength conversion

WAN Wide Area Network

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