Monitored network data allows operators to gain
valuable insight into the health and status of a
network. Whilst such data is useful for real-time
analysis, there is often a need to post-process
historical network performance data. Storage of the
monitored data then becomes a serious issue as
network monitoring activities generate significant
quantities of data.
The work in this thesis is motivated by the need of
measuring the performance of high-speed networks.
Such networks produce large amounts of data over a
long period of time, making the storage of this
information practically inefficient. A possible
solution to this problem is to use lossy compression
on an on-line system that intelligently compresses
computer network measurements while preserving the
quality in important characteristics of the signal.
This thesis contributes to the knowledge by examining
two threshold estimation techniques, two threshold
application techniques and the impact of window size
on the lossy compression performance. In addition
eight different wavelets were examined in terms of
compression performance, energy preservation, scaling
behaviour, quality attributes and Long Range Dependence.