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|Authors: ||Kriege, Jan|
|Title: ||Fitting simulation input models for correlated traffic data|
|Abstract: ||The adequate representation of input models is an important step in building
valid simulation models. Modeling independent and identically distributed data
is well established in simulation, but for some application areas like
computer and communication networks it is known, that the assumption of
independent and identically distributed data is violated in practice and that
for example interarrival times or packet sizes exhibit autocorrelation over a
large number of lags. Moreover, it is known that negligence of these
correlations can result in a serious loss of validity of the simulation model.
Although different stochastic processes, which can model these autocorrelations,
like e.g. Autoregressive-To-Anything (ARTA) processes and Markovian Arrival
Processes (MAPs), have been proposed in the past and more recently fitting
algorithms to set the parameters of these processes such that they resemble
the behavior of observations from a real system have been developed,
the integration of correlated processes into simulation models is still a challenge.
In this work ARTA processes are extended in several ways to account for the
requirements when simulating models of computer and communication systems.
In a first step ARTA processes are extended to use an Autoregressive Moving
Average (ARMA) process instead of a pure Autoregressive (AR) base process to
be able to capture a large number of autocorrelation lags, while keeping
the model size small. In a second step they are enabled to use the flexible
class of acyclic Phase-type distributions as marginal distribution.
To support the usage of these novel processes in simulation models
a fitting algorithm is presented, software for fitting and simulating
these processes is developed and the tools are integrated into the
toolkit ProFiDo, which provides a complete framework for fitting and
analyzing different stochastic processes.
By means of synthetically generated and real network traces it is shown that
the presented stochastic processes are able to provide a good approximation
of the marginal distribution as well as the correlation structure of the
different traces and result in a compact process description.|
|Subject Headings: ||ARTA processes|
Phase type distributions
|Issue Date: ||2012-02-15|
|Appears in Collections:||LS 04 Quantitative Methoden, Rechnernetze und verteilte Systeme, Rechnersysteme und Leistungsbewertung|