insituto tecnologico y de estudios superiores de monterrey

insituto tecnologico y de estudios
superiores de monterrey
campus monterrey
division de tecnologias de la informacion y electronica
a performance comparison of contention resolution and
resource allocation in slotted optical burst switched
vs. optical packet switched network scenarios
Proyecto de Fin de Carrera presentado como requisito
parcial 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, su
apoyo y su ayuda en todo momento.
A Miguel Bautista León, por su trabajo en el CET que permitió el
desarrollo del mı́o y por su colaboración.
Al Dr. Gerardo Castañón, por su disponibilidad a la hora de revisar este
documento.
A Iván Razo-Zapata, por sus valiosos comentarios en el desarrollo del
proyecto.
A Alberto Herrera, por su amabilidad y ayuda.
Al Instituto Tecnológico y de Estudios Superiores de Monterrey, por las
facilidades provistas.
2
List of Figures
2.1
2.2
2.3
2.4
OPS Network . . .
OBS Network . . .
OPS Architecture .
SOBS Architecture
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3.1
European Optical Network
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SOBS performance for Poisson traffic . . . . . . . . . . . . . .
Switch OPS vs. Switch SOBS under Poisson traffic . . . . . .
Switch OPS vs. Switch SOBS under exponential traffic . . . .
Switch OPS vs. Switch SOBS under Pareto traffic . . . . . .
Fibers under Poisson distribution with a traffic load of 0.8 . .
FDLs under Poisson distribution with a traffic load of 0.8 . .
Fibers under exponential distribution with a traffic load of 0.8
FDLs under exponential distribution with a traffic load of 0.8
Fibers under Pareto distribution with a traffic load of 0.5 and
H = 0.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.10 FDLs under Pareto distribution with a traffic load of 0.5 and
H = 0.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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4.1
4.2
4.3
4.4
4.5
4.6
4.7
4.8
4.9
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26
27
Contents
1 Introduction
1.1 Objectives . . . . .
1.2 Problem Approach
1.3 Hypothesis . . . .
1.4 Justification . . . .
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2 Theoretical background
2.1 OPS, OBS and SOBS theoretical background
2.1.1 OPS . . . . . . . . . . . . . . . . . . .
2.1.2 OBS . . . . . . . . . . . . . . . . . . .
2.1.3 SOBS . . . . . . . . . . . . . . . . . .
2.1.4 SOBS previous work . . . . . . . . . .
2.2 OPS switch model in which SOBS is based .
2.2.1 Opsim . . . . . . . . . . . . . . . . . .
2.3 SOBS Switch design . . . . . . . . . . . . . .
2.4 Traffic models . . . . . . . . . . . . . . . . . .
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3 Methodology
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3.1 Description of the burst aggregator . . . . . . . . . . . . . . . 16
3.2 Basis for OPS and SOBS comparison . . . . . . . . . . . . . . 18
4 Simulations and Results
4.1 SOBS switch . . . . . . . . . . .
4.2 OPS switch vs. SOBS switch . .
4.2.1 Poisson distribution . . .
4.2.2 Exponential distribution .
4.2.3 Pareto distribution . . . .
4.3 OPS network vs. SOBS network
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5 Conclusions and future work
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5.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
5.2 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
A Acronyms
31
3
Chapter 1
Introduction
The use of Internet is growing together with the amount and size of the data
shared through it. Emerging dynamic high-bandwidth network applications
require the implementation of networks capable of supporting the necessary
bandwidth for them. The emergence of optical communication technology
has led to the extended implementation of Optical fiber networks that are
capable of meeting this need. Although early deployments involved optical
fiber links and optical-electronic-optical switches in order for the information
to be processed electronically, late deployments started to make use of alloptical switching technology, to take advantage of the possibility of avoiding
the costly conversion to electronics at intermediate nodes.
Firstly deployed optical networks primarily employed optical circuit switching [1]. In these networks, a lightpath is established between source and destination node before the data is transmitted and therefore it requires roundtrip signalling to reserve the necessary resources. They are well suited for
persistent high-bandwidth traffic that does not vary much over time; however it does not suit that well Internet applications dynamic bursty traffic.
Circuits tend to be static and provide a fixed amount of bandwidth, so they
do not make efficient use of the resources. Furthermore, additional inefficiency and overhead are produced as a result of the transmission time for
the data transfer being smaller than to the round trip propagation delay
required by the signalling in many on-demand data transfers.
Because of these disadvantages, much research has been conducted to
overcome them towards the development of Optical Packet Switching (OPS)
and Optical Burst Switching (OBS). Both proposals attempt to use resources only when the data is being transmitted, thereby providing statistical multiplexing and a higher degree of utilization than optical circuit
4
switching.
In recent years, Slotted Optical Burst Switching (SOBS) has been proposed as an alternative to OPS and OBS joining some of the advantages of
both of them. The simplicity of handling synchronized information at nodes
joins the reduction of overhead small units of data would present.
At the same time, in the traditional traffic model, packets and interarrival rates are often assumed to be a Poisson process, due to the simplicity
of its mathematical model and its low correlation between its interarrival
times, which translates in the process having low memory [2]. In the beginning of the 90’s additional traffic studies were reported which showed a
packet interarrival distribution different from exponential [3], and this traffic
behavior is better modeled using self-similar probability distributions. Because of this, further studies are required to analyze the performance of all
the proposed models under this conditions which reflect in a more reliable
way 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 of
packet loss probabilities under similar conditions of traffic for three
different traffic models: Poisson, Exponential and Pareto
• Compare the performance of the European optical network based on
each type of switch under different traffic models according to the costs
associated to the utilization of resources
1.2
Problem Approach
The fast growth of Telecommunication services has increased the requirements of bandwidth globally. Optical networks seem to be a good approach
to solve this problem. However, despite their numerous advantages, they
still need the development electronics have reached throughout the years.
Difficulties like ineffective and expensive optical memories make it necessary to find new approaches to networking and the behavior of the nodes
to avoid blocking situations. Therefore past and renewed switching models
need to be reviewed and applied to the optical domain, in order to minimize information loss, administer the network and address future quality of
service Quality of Service (QoS) requirements.
5
The models proposed up to date show some drawbacks which need to be
addressed. OPS has a large overhead associated to short data units which
affects its efficiency. Furthermore, optical converters are not as developed as
electrical ones requiring an stabilization time, so the use of Total Wavelength
conversions (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 dropped
thus increasing the packet loss rate. In addition to this, packet delay might
be critical for some applications and OBS increases it due to burst aggregation time.
1.3
Hypothesis
SOBS might be a good alternative to the already proposed OPS and OBS
models. It has not been plenty studied yet and it might benefit from lower
requirements for the TWCs and solve packet header overhead inefficiency,
yet maintaining the advantages derived from synchronicity, thus postulating
as a valid solution.
1.4
Justification
An increasing load of traffic is being shared through internet as people utilization rises and so does the size of the data transmitted. Optical fiber
networks are necessary to provide with the demanded bandwidth as traditional electronic networks no longer can meet the growing requirements.
And if all the potential is intended to be taken advantage of, all-optical networks can offer a less costly alternative than optical fiber links connected to
electronic switches.
Still, this kind of networks is not by far fully developed. Switching
models are being reviewed in order to provide the most suitable solution to
the advantages and limitations optical domain offers. First circuit switching
and then OPS and OBS were proposed, and currently further research about
the last two is being held. Additionally SOBS was proposed as a way to try
to get together the advantages of both OPS and SOBS. Finding the best
possible solution for optical switching is critical in order to exploit all the
possibilities offered by this technology. However, the effectiveness of this
solution when compared with the others is still to be proved so that it can
really be considered. Simulating its behavior under different conditions will
help to know the steps to take in order to optimize its performance. In
6
addition, hidden considerations which must be taken care of will be easier
to analyze, keeping always in mind the real deployment of this networks and
the drawbacks which might not in the beginning be so evident.
Furthermore, the model of traffic used to test the nodes (and obviously
the networks) will also be critical in order to accept the results of their
performance. This is, the broader the study of the technology the better.
Including self-similar traffic in the research guarantees nowadays a more
realistic assessment of the real capacity of the solution.
7
Chapter 2
Theoretical background
2.1
OPS, OBS and SOBS theoretical background
As mentioned above, the first approach to optical domain only included
optical fiber links but kept making use of electronic switches, so there was
a waste of the capacity due to the optical-electronic-optical conversions and
the decrease in the speed working in the electronic domain implies.
The first approach (optical circuit switching) was already discussed. Its
characteristics made it unsuitable for dynamic applications such as the ones
dominating the internet nowadays. As a result, much work has been conducted toward the development of OPS and OBS networks in order to avoid
the inefficient use of the resources [1].
2.1.1
OPS
An OPS consists of a packet-by-packet switching in the optical domain without conversion to electronics at intermediate nodes as shown in Figure 2.1.
Each packet contains the control information, thus offering the most efficient
utilization of the bandwidth. Nevertheless, the success of OPS relies heavily
on device technology and properly designed architectures for providing a
set of basic functionalities required for switching packets in the optical domain. These functionalities include packet synchronization, packet header
processing, switching and contention resolution. Furthermore, it would need
to 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, OBS
has been proposed. The underlying idea is collecting data into bursts and
switching 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, reducing signalling overhead and reducing buffering of data at intermediate
nodes. On the other hand the process of creating bursts translates in a
delay in individual packets that must be taken into account in certain applications. The one way reservation mechanism reduces the signalling overhead
compared to optical circuit switching.
2.1.3
SOBS
Trying to obtain the benefits from OBS while reducing the data loss, Slotted
OBS SOBS was introduced. It is also known as Synchronous OPtical Burst
Switching (SyncOBS) by other authors, but the concept is essentially the
same.
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. And
given the asynchronous character of the bursts, a situation might happen
when a burst is only partially blocked, but still the entire content of the burst
must be dropped. Obviously this leads to an inefficient resource utilization
of outgoing wavelengths.
Alternatively, if optical buffering resources are available at intermediate
nodes and two arriving bursts compete for an outgoing wavelength, the first
one will get it while the second one is stored. However, the most common
optical buffering, Fiber Delay Lines (FDLs), consist on a simple optical fiber
attached to the node, thus providing with a fixed duration determined by its
length. Even if the first burst finishes quickly, the second burst is delayed
by the FDLs entire duration before it is forwarded out to the outgoing
wavelength delaying the blocked burst more than necessary and inefficiently
utilizing the outgoing wavelength.
For these reasons SOBS is expected to achieve better resource resolution
by using synchronous timeslots which avoid the situations mentioned above.
It should be taken into account that this technique requires extra mechanisms to provide synchronization at each node, and still suffers from an
individual packet delay worse than in OPS. This last option can also be
10
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 lot
of research about it. It was confirmed that SOBS with fixed-length bursts
shows smaller data loss than conventional OBS [5]. In another work related
to QoS it was demonstrated the importance of SOBS, given that it shows
similar behavior than OBS with fewer wavelengths with a relatively high
traffic load [6]. Once the convenience of SOBS was assumed, plenty of work
focused on optimizing its implementation, for example by finding the best
size 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 an
OPS simulator used in [7]. And it is also used to make the comparisons
between both models of switching, OPS and SOBS. Thus, a description of
the simulator is stated below.
2.2.1
Opsim
Opsim is a simulator of an optical packet switch whose architecture is shown
in Figure 2.3.
Programmed in Programming language utilized (C++), it describes the
operation of a switch with the possibility of buffering the packets by means
of FDLs or converting them to other wavelengths by means of TWCs in
order to avoid packet loss in blocking situations.
Parameters as the simulation time, number of inlets, outlets, wavelengths, FDL length, number of TWCs, or blocking solving strategy can
be chosen by the user. Specifically, two blocking solving strategies are offered. When a blocking situation occurs, wavelength conversion prior to
packet buffering denominated Minimum packet buffering strategy (minBuff)
can be the preferred method to solve it, or packet buffering prior to wavelength conversion denominated Minimum conversions strategy (minConv)
can be chosen. When there are not enough resources available to relocate
the packet, it is dropped.
Originally it was designed to be fed (injection of all the inlets) in each
time slot (i. e.: synchronously) with traffic uniformly distributed with a
certain 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 packet
loss and its causes could later be analyzed.
Later on, different types of traffic were added to feed the OPS switch to
evaluate its performance, including exponentially distributed, Poisson and
Pareto.
In addition, a network was also deployed in order to study the behavior of
the switch in a real scenario. Optical networks such as the European were
studied with real parameters of distances, nodes and connections. Apart
from this, all the parameters including number of fibers or number of wavelengths 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 on
SOBS and its performance. The design of the switches varies greatly, but the
vast majority of them evaluate the behavior of the switch to route the bursts,
without taking care of their actual conformation. This is, that process was
omitted together with the influence it had on the whole performance. [[8]].
From all these, some were modeled using a simulation program were the
12
parameters were introduced [8], [9]. Some others were based on simulator
specially designed for the research[10]. Among the ones with their own
simulator is the one on which the current work is based. In [4] describe the
working mode of a SOBS optical switch as:
Event : : a p a c k e t a r r i v e s
i f ( t i m e r t i s not s t a r t e d ) {
r e s t a r t timer 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$ ) {
s c h e d u l e t h e data b u r s t t o be s e n t out ;
stop timer t ;
reset buffer \ size ;
}
Event : : t i m e r t = T
s c h e d u l e t h e data b u r s t t o be s e n t out ;
stop timer t ;
reset buffer \ size ;
As we can see, two events can trigger the creation of a burst: the completion of the timeslot length, given a maximum size for it; or the end of the
timer. The maximum size needs not to be completed exactly to match, but
just when an incoming packet arrives which would make exceed the length
of the burst, then a burst is created and that packet will be the first in the
next burst. As for the timer, it is reset every time a burst is generated, and
works just as a way to avoid too long a delay in the packets. The resulting
switch architecture is shown in Figure 2.4.
2.4
Traffic models
The switch performance was evaluated against different types of traffic models. First of all an interarrival time following Poisson distribution was under
study. According to this distribution, the probability of k occurrences during
a period of time is defined in Equation 2.1.
λx e−λ
(2.1)
k!
λ is a positive real number, equal to the expected number of occurrences
during 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
f (k, λ) =
13
Figure 2.4: SOBS Architecture
load can be controlled by the choice of this parameter given that traffic load
equals the number of events (f(lambda)) in a period of time.
Poisson distribution is the most commonly used in previous SOBS work
as it is memory-less and consequently mathematically easy to describe. It
allows to be modeled analytically before actual simulations are done, that
being the reason why it is so extended its use.
In addition to this, exponentially distributed interarrival time was explored, 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 a
period of time, e is the base of the natural logarithm (e = 2.71828...) and x
is the time. As with Poisson distribution, the traffic load can be determined
by the λ parameter.
This distribution is typically used in queuing theory to describe the service 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 therefore to mathematically analyze.
14
And finally, Pareto distributed traffic was under study. Recent work
shows how self-similar traffic describes more realistically different types of
network traffic: web services, IP traffic, video traffic, Metropolitan Area
Network (MAN) and Wide Area Network (WAN), among others. [11]. Some
of the main factors that can produce the Long Range Dependence (LRD) of
different 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 patterns
repeated through time. This is the traffic somehow works with memory; the
previous states of the network influence its present state.
Mathematically we have a random signal whose statisticians met the
requirements defined in Equations 2.3, 2.4 and 2.5.
E[X(αt)]
(2.3)
αH
V ar[X(αt)]
V ar[(X(t)] =
(2.4)
α2H
Rx (αt, αs)
Rx (t, s) =
(2.5)
α2H
Therefore, the degree of self-similarity is defined by an only value, the
H parameter or Hurst parameter, which is directly related to the form parameter of a Pareto distribution by Equation 2.6:
E[(X(t)] =
3−a
(2.6)
2
In the case of LRD H is between 0.5 and 1, which means values of a
between 2 and 1. H = 0.5 indicates lack of self-similarity, whereas for H=1
the randomness disappears.
Therefore we can generate self-similar traffic form a Pareto distribution
with a ∈ (1, 2) [13]. It provides a method to analyze systems in a more
realistic scenario, given the fractal nature of real traffic.
H=
15
Chapter 3
Methodology
Based on the high-level language solution described above, a burst generator
was developed. In order to make use of previous work, the next idea was
proposed: as the SOBS was characterized by making a synchronous burst
out of asynchronous packets, it would be possible to treat burst as if they
were packets of a certain length inside the switch already implemented. The
only necessary thing was to take into account the size of the elements as they
would 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 corresponding
statistical distribution (Poisson, Exponential, Uniform or Pareto) until the
sum of them exceeds the time assigned to the timeslot. The number of packets which will be generated in that timeslot will thus be equal to those whose
interarrival times fit within the timeslot duration. Sizes can be assigned to
those packets.
For the bursts to be created the sizes of the packets are iteratively
summed 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 this
construction of the switch consists of an empty object. When that number
is reached during a timeslot, the remaining packets are saved to be added in
the next timeslot, while a burst is generated with length equal to the total
accumulated length of that burst. In order to reduce delays in case it takes
too long for a burst to be complete, there is a timer by which even if the
required length is not reached, the burst is generated anyway and sent at
the end of the timeslot (so timer is an integer number of timeslots).
16
This burst generator was included in the switch simulator to generate the
traffic 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 different traffic models and varying the resources available at the switch. The
packet loss against the traffic load was the parameter chosen to represent
the switch performance. The simulations were made for the different traffic distributions so that it was possible to compare them. The time-unit
is 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 network shown in Figure 3.1 mentioned above among the previous work in order
to evaluate its performance when being part of a real network. Simulations
to determine the use of resources by the network were made, in terms of
fibers and delay lines to add in order to achieve lack of packet loss.
Figure 3.1: European Optical Network
17
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 by
each of them which are not so evident to allocate. Here is the criteria used
to make the comparison as fair as possible.
Simulation time. The same total simulation time was employed for both
of 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 in
SOBS.
TWC. Although the length of the data processed by each of them is different, still the number of TWC assigned to both was the same, one per
optical outlet.
FDL. The number of FDLs assigned was based on the fiber length without
taking into account other parameters such as processing complexity.
This way, 1 FDL for SOBS has a length of 10 time-units (the size of
the timeslot). On the other hand, for OPS the first FDL will have
length = 1 time-unit, the second one will have length 2 (so that units
can 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 employing
1 + 2 + 3 + 4 = 10 time-units of resources. This way, the comparison
is made with 1 FDL for SOBS vs. 4 FDL for OPS.
18
Chapter 4
Simulations and Results
The results shown in next sections will help to make a comparison between
the performance of OPS and SOBS. First the results of packet loss for
the SOBS switch alone are detailed. Then OPS and SOBS are simulated
under the same conditions in a switch, and finally their performance in the
European network is evaluated.
4.1
SOBS switch
SOBS switch was tested for 108 simulation units, combined in 107 slots of
10 time-units each. For a 4 x 4 switch with 4 wavelengths on each fiber
data was generated according to several distributions, conformed in bursts
of length 10 and then introduced in the switch with a randomly chosen
destiny. Once in the switch, conversion was the primary method to avoid
blocking situations, minBuff, with 16 converters, followed by 0, 1, and 2
optical 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 performance. The load varied from 0.1 to 0.9 and the number of TWCs was
invariable, as many as optical outputs. Configurations of 4 x 4 and 8 x
8 switch with 4 or 8 wavelengths were tested. The larger the number of
wavelengths, the lower the packet loss rate. So the more wavelengths are
included in each fiber the more efficient will be the use of the resources to
solve blocking situations.
19
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 4
wavelengths for 108 simulation units, combined in 107 slots of 10 units each.
In case of SOBS the solving blocking situations mode was minBuff, whereas
for 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 tested
for Poisson distributed traffic with different amount of resources for OPS
and SOBS switch. The use of FDLs is critical to achieve a low enough
packet loss rate for both types of switches, although with no FDL available
SOBS has a better performance. SOBS can not achieve the performance
of OPS when FDLs are used, although the use of the converters would be
much easier for long bursts than for single packets.
20
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 tested
for exponentially distributed traffic with different amount of resources for
OPS and SOBS switch. As with Poisson distributed traffic, SOBS cannot
outperform OPS when FDLs are included. Thus it seems under these model
of 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 0.9
was tested for Pareto distributed traffic with different amount of resources
for OPS and SOBS switch.
The results show how with OPS high correlation (values of H close to
1) translates into much higher packet loss rate, while SOBS tends to soften
this tendency making the loss rate similar for every value of H. This is
supported in previous works such as [14] where it was demonstrated that
burst switching reduces the self-similarity of the traffic when compared to
packet switching, therefore outperforming OPS. Still the burst aggregate
process might be to blame of this effect as big bursts of packets generated
21
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 to
measure 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]. The
dimensioning process occurs in two steps. During the first one, stabilization,
every time a blocking situation occurs, the switch tries to solve it with the
available converters and buffers. If blocking cannot be solved there is an
increment of the buffer depth by one unit. When the number of delay lines
reaches the limit (in this case, 3), the number of fibers for that specific link is
increased. At the beginning the buffers depth is 0. This part of the process
lasted for 40000 time-units.
After this, the actual simulation took place with the resources calculated
previously. With a duration of 1000 time-units, this part was useful to check
if the stabilization process had been long enough, as no more packets could
be dropped, as was the case.
22
Figure 4.4: Switch OPS vs. Switch SOBS under Pareto traffic
The topology used has 19 nodes. Each of them has an injection fiber at
one of its inlets and an absorption fiber at one of its outlets. The rest of the
inlets and outlets were connected to other nodes as shown in the topology of
Europe in Figure 3.1. The destinies of the packets were chosen randomly and
the routing was determined by an algorithm of shortest distance, solving the
blocking situations by minBuff mode for both types of switch architecture.
Results of network dimensioning are shown next using the topology of
Europe in Figure 3.1 in terms of fibers per node and number of delay lines
using SOBS and OPS nodes as the ones described above.
For Poisson traffic with a traffic load of 0.8 the results are shown in
Figure 4.5 for the fibers and Figure 4.6 for the delay lines. There we can
see how the number of fibers is not affected by the type of switch used, as
both of them require the use of 90, whereas there is an improvement in the
number 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 in
Figure 4.7 for the fibers and Figure 4.8 for the FDL. The need for resources
is 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-
23
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
24
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
25
rameter of 0.8 the results are shown in Figure 4.9 for the fibers and Figure
4.10 for the FDLs. Here is where we can find the bigger differences for OPS
and SOBS. Although the number of FDLs is very close, 61 for SOBS and 63
for OPS, the number of fibers required is remarkably smaller for SOBS,78
vs 81 for OPS thus making it more efficient. Still these data needs to be
considered carefully as mentioned above for the effect the burst creator has
on self-similarity.
Figure 4.9: Fibers under Pareto distribution with a traffic load of 0.5 and
H = 0.8
26
Figure 4.10: FDLs under Pareto distribution with a traffic load of 0.5 and
H = 0.8
27
Chapter 5
Conclusions and future work
5.1
Conclusions
A study into the performance features of OPS and SOBS nodes and networks
was performed. Different traffic distributions were developed to allow the
comparison between those. Following, an analysis on the results is detailed
in order to give an a solution to the problem statement provided in the first
chapter:
SOBS switch. The SOBS switch was implemented based on an OPS switch
in order to study its behavior. It was shown how the packet loss rate
diminishes as the number of wavelengths increases. So a bigger number of wavelengths makes the use of the resources more efficient to
solve blocking situations.
OPS and SOBS switch comparison under different traffic distributions.
Both switches were tested under different traffic conditions. For Poisson and exponentially distributed traffic OPS switch proved to be the
best option, as SOBS switch was unable to outperform it. However,
under Pareto distributed traffic, SOBS reduced the packet loss rate for
high values of H parameter under the circumstances previously mentioned for the burst creator. We can conclude that SOBS mitigates
the effect self-similar traffic has on switch performance, thus making
the dimensioning of switch resources more reliable and improving its
performance under highly self-similar traffic. In more general terms
SOBS would become the best candidate for real life optical networks
given their demonstrated high self-similarity
28
OPS and SOBS switch processing complexity comparison. Although
the switch performance comparison was made under similar resource
allocation, only their FDL length was taken into account. For the
analysis to be more accurate, processing costs would have to consider
also the fact of processing much bigger data units in each operation
(burst vs. packets) making it more efficient or the convenience for
current TWCs to process bigger data units so that the stabilization
time is not so critical a parameter.
OPS and SOBS network comparison. Again both architectures were
tested under different traffic conditions. Under Poisson traffic the resources required were similar, with a slight improvement in the number
of FDLs for SOBS. Just the opposite as with exponential. Therefore,
the implementation of SOBS switches under these traffic distributions
is not justified unless an extended study is made on SOBS effective
resource consumption. However, under the same conditions of the
switch, self-similar traffic shows a better performance in SOBS network, 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 must
consider:
Traffic distributions. Preservation of the traffic distribution patterns throughout the network and exploitation of the possibilities aggregating in
bursts offers, especially for self-similar traffic has not been studied.
Packet delay. Thorough examination of packet delay in network scenarios
for SOBS is required. It can represent a critical parameter when QoS
is required and a reason to reconsider the use of OPS as the packets
do not suffer additional delay.
Quality of Service. The introduction of granted Quality of Service to
both OPS and SOBS represents a promising topic of study.
Strategies. Defined strategies should be applied to network scenarios according to the results obtained, intending to save resources by getting
advantage of the network traffic distribution.
29
Computing complexity. Additional research is required to model the advantages/disadvantages of OPS, OBS and SOBS in terms of computing
complexity at each node.
Energy consumption. As a consequence of the computing complexity
mentioned above, energy requirements need to be determined as they
play a critical role in implementation.
30
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
31
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