Link Delay Inference in ANA Network
Master thesis
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https://hdl.handle.net/10642/433Utgivelsesdato
2008Metadata
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Sammendrag
Estimating quality of service (QoS) parameters such as link delay distribution from the
end-to-end delay of a multicast tree topology in network tomography cannot be achieved without
multicast probing techniques or designing unicast probing packets that mimic the characteristics
of multicast probing packets. Active probing is gradually giving way to passive
measurement techniques. With the emergence of next generation networks such as Autonomic
Network Architecture (ANA) network, which do not support active probing, a new way of thinking
is required to provide network tomography support for such networks. This thesis is about
investigating the possible solution to such problem in network tomography. Two approaches,
queue model and adaptive learning model were implemented to minimized the uncertainty in
the end-to-end delay measurements from passive data source so that we could obtain end-toend
delay measurements that exhibit the characteristics of unicast or multicast probing packets.
The result shows that the adaptive learning model performs better than the queue model. In
spite of its good performance against the queue model, it fails to outperform the unicast model.
Overall, the correlation between the adaptive learning model and multicast probing model is
quite weak when the traffic intensity is low and strong when the traffic intensity is high. The
adaptive model may be susceptible to low traffic. In general, this thesis is a paradigm shift
from the investigation of ”deconvolution” algorithms that uncover link delay distributions to
how to estimate link delay distributions without active probing.
Beskrivelse
Master i nettverks- og systemadministrasjon
Utgiver
Høgskolen i Oslo. Avdeling for ingeniørutdanningUniversitetet i Oslo