The Internet and associated network technologies are an increasingly integral part
of modem day working practices. With this increase in use comes an increase in
dependence. For some time commentators have noted that given the level of reliance on
data networks, there is a paucity of monitoring tools and techniques to support them. As
this area is addressed, more data regarding network perfonnance becomes available.
However, a need to automatically analyse and interpret this perfonnance data now
becomes imperative. This thesis takes one-way latency as an example perfonnance
metric. The tenn 'Data Exception' is then employed to describe delay data that is unusual
or unexpected due to some fundamental change in the underlying network perfonnance.
Data Exceptions can be used to assess the effect of network modifications and failures
and can also help in the diagnosis of network faults and perfonnance trends. The thesis
outlines how Data Exceptions can be identified by the use of a two-stage approach. The
Kolmogorov-Smirnov test can initially be applied to detect general changes in the delay
distribution, and where such a change has taken place, a neural network can then be used
to categorise the change. This approach is evaluated using both a network simulation and
a test network to generate a range of delay Data Exceptions.
A Doctoral Thesis. Submitted in partial fulfilment of the requirements for the award of Doctor of Philosophy of Loughborough University