4,058 research outputs found
Traffic measurement and analysis
Measurement and analysis of real traffic is important to gain knowledge
about the characteristics of the traffic. Without measurement, it is
impossible to build realistic traffic models. It is recent that data
traffic was found to have self-similar properties. In this thesis work
traffic captured on the network at SICS and on the Supernet, is shown to
have this fractal-like behaviour. The traffic is also examined with
respect to which protocols and packet sizes are present and in what
proportions. In the SICS trace most packets are small, TCP is shown to be
the predominant transport protocol and NNTP the most common application.
In contrast to this, large UDP packets sent between not well-known ports
dominates the Supernet traffic. Finally, characteristics of the client
side of the WWW traffic are examined more closely. In order to extract
useful information from the packet trace, web browsers use of TCP and HTTP
is investigated including new features in HTTP/1.1 such as persistent
connections and pipelining. Empirical probability distributions are
derived describing session lengths, time between user clicks and the
amount of data transferred due to a single user click. These probability
distributions make up a simple model of WWW-sessions
A critical look at power law modelling of the Internet
This paper takes a critical look at the usefulness of power law models of the
Internet. The twin focuses of the paper are Internet traffic and topology
generation. The aim of the paper is twofold. Firstly it summarises the state of
the art in power law modelling particularly giving attention to existing open
research questions. Secondly it provides insight into the failings of such
models and where progress needs to be made for power law research to feed
through to actual improvements in network performance.Comment: To appear Computer Communication
Global Modeling and Prediction of Computer Network Traffic
We develop a probabilistic framework for global modeling of the traffic over
a computer network. This model integrates existing single-link (-flow) traffic
models with the routing over the network to capture the global traffic
behavior. It arises from a limit approximation of the traffic fluctuations as
the time--scale and the number of users sharing the network grow. The resulting
probability model is comprised of a Gaussian and/or a stable, infinite variance
components. They can be succinctly described and handled by certain
'space-time' random fields. The model is validated against simulated and real
data. It is then applied to predict traffic fluctuations over unobserved links
from a limited set of observed links. Further, applications to anomaly
detection and network management are briefly discussed
The pseudo-self-similar traffic model: application and validation
Since the early 1990¿s, a variety of studies has shown that network traffic, both for local- and wide-area networks, has self-similar properties. This led to new approaches in network traffic modelling because most traditional traffic approaches result in the underestimation of performance measures of interest. Instead of developing completely new traffic models, a number of researchers have proposed to adapt traditional traffic modelling approaches to incorporate aspects of self-similarity. The motivation for doing so is the hope to be able to reuse techniques and tools that have been developed in the past and with which experience has been gained. One such approach for a traffic model that incorporates aspects of self-similarity is the so-called pseudo self-similar traffic model. This model is appealing, as it is easy to understand and easily embedded in Markovian performance evaluation studies. In applying this model in a number of cases, we have perceived various problems which we initially thought were particular to these specific cases. However, we recently have been able to show that these problems are fundamental to the pseudo self-similar traffic model. In this paper we review the pseudo self-similar traffic model and discuss its fundamental shortcomings. As far as we know, this is the first paper that discusses these shortcomings formally. We also report on ongoing work to overcome some of these problems
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