Contents Telektronikk - Telenor

This in order to test the effect of these on
the network performance and the QoS
before decisions are made. Hence, in
order to perform traffic measurements of
ATM systems, it is necessary to be able
to generate an artificial traffic load which
has characteristics which are close to real
(current and expected) traffic. For details,
see the separate box High level requirements
for an ATM traffic load generator.
The recognition of the above facts
triggered our work on source modelling
and traffic generation. Traffic generation
must be based on one or more models of
the source. Classes of models and generation
principles are discussed in the next
section, together with the composite
source model (CSM) on which the synthesised
traffic generator (STG) is based.
This generator, which currently is the
most advanced ATM traffic generator
available, is presented in Section 4.
Before that, Section 3 gives a brief introduction
to how source type models may
be defined in the composite source model
framework. An outlook and some concluding
remarks end the paper.
2 Models for load
generation
2.1 Source type modelling
Before proceeding to modelling of
sources for load generation, a brief
overview of different types of source
models is necessary. The subsections
below are neither claimed to be a complete
review of the state of the art, nor
perfect in their classification.
2.1.1 State based models
The most common approach to ATM
source type modelling is to assume that
there is some sort of finite state machine
(FSM) behaviour determining the behaviour
of the source. For some types of
sources, for instance for speech encoded
with silence detection, this is a correct
assumption. For other source types, for
instance variable bitrate coded (VBR)
video, it is an artefact used to approximate
an infinite state behaviour of the
source. This will be dealt with in
Section 3.
There are two different approaches in
state based modelling. Either the cell
generation process is modelled by a finite
state machine directly, or the cell generation
process is modulated by a finite state
machine.
λ 3
λ 2
λ 1
λ 3
λ 2
λ 1
Average cell rate
λ 3
λ 2
λ 1
Sojourn time in state 2
Figure 2.1 Illustration of a simple modulating finite state machine
Probability density
Modulation of the cell process
This approach reflects the multilevel
modelling which will be discussed later
in this section. The basic idea is that a
finite state machine modulates the expected
rate (mean value) of an underlying
process. This is demonstrated by a
very simple example in Figure 2.1.
The state machine is usually assumed to
have the Markov or semi-Markov property,
i.e. its sojourn times in the states
and the transitions to the next states are
completely independent of its previous
behaviour. The source type models of
this class differ in the following aspects:
λ 3
λ 2
λ 1
General
λ 3
λ 2
λ 1
Phase type
Figure 2.2 Sketch of different types of sojourn time distributions
Time
Negative exponential
Sojourn time
Sojourn time distribution; determines the
duration of the levels in the modulated
flow. The sojourn times are in most models
negative exponentially distributed
which makes the state machine a discrete
state continuous time Markov model. It is
also proposed to use general distributions,
which makes it a semi-Markov
model. As a trade-off between modelling
power/accuracy and mathematical
tractability, phase type distributions are
suited. See [2] for an introduction. This
type of sojourn time is used in the source
type model used in the STG, cf. Section
2.4.1. The different types are illustrated
in Figure 2.2.
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