This talk is the latest of my talks about FETA the framework for evolving topology analysis. This uses updated notation. The core of the work is a likelihood based model which can assess how likely it is that observations of the evolution of a graph arise from a particular probabilistic model, for example a model such as the Barabassi-Albert preferential attachment model. Analysis is given to data from Facebook and from Enron as well as from artificial models.
Research about modelling aspects of networks.
This paper looks at a markov chain based model and uses queuing theory to analyse its performance. The system is D-BMAP/D/1 and a closed form solution is found
This paper looks at the phenomenon of long-range dependence. It shows that certain long-range dependent models give answers which contain infinities and also that this behaviour will not be detected by a naive modelling approach. The work is an extension of an earlier published PMECT paper.
This paper creates a simple mathematical model based on Markov chains which can model (with some simple assumptions) the type of cacheing trees seen in content centric networking. The model is tested with some simulation results.
This paper considers the problem of balancing traffic across network egresses. It achieves a workable solution using a scalable packet market scheme which couples end-hosts controlling their own connection with an overall controller which can select routes appropriately for each flow.
The flows are balanced to seek paths which minimise loss.
This paper creates software models of how P2P network topologies could wire up. It considers the possible strategies such as connecting to close nodes, connecting to random links and so on. Resilience and delay are considered.
This paper looks at the implementation of a live streaming peer-to-peer video system. The system is a hybrid which can cope with live streaming and video on demand.