Cristian Zilli
Understanding traffic matrix estimation with eXplainable AI (XAI).
Rel. Guido Marchetto, Alessio Sacco. Politecnico di Torino, Master of science program in Computer Engineering, 2022
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Abstract
Diagnostics constitute a foundation for the management, maintenance, and improvement of computer networks. To this end, traffic matrices are an effective element of diagnostics by representing directed traffic flows between pairs of networked nodes in a compact manner. In detail, a traffic matrix is a two-dimensional array where each row (column) corresponds to a node, and each cell contains the value of traffic flow between the row and column nodes, obtained by aggregating link load measurements over a sampling time interval. The collection of this data serves the purpose of enacting strategies for infrastructural enhancement and traffic engineering in a conscientious, informed way.
However, such information can often be only partially available: this is the case, for example, of networks dealing with massive volumes of traffic such that telemetry operation may put heavy computational strain on the measuring devices, causing these to suffer degradation of performance for their networking functions (e.g
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