Abstract
Network Traffic Management includes all the procedures set up for recognizing and solving traffic anomalies, i.e., any mismatching situation between traffic demand and network capacity that leads to an impairment in network performances. The resolution of traffic anomalies has generally received considerably more attention than the recognition phase. In this paper a diagnostic tool, based on the use of fuzzy logic, is proposed to recognize traffic anomalies (overloads and failures) on the basis of measurements currently available on commercial switches. The tool is composed of five separate inferential blocks, arranged over three hierarchical levels. The probability of correct diagnosis in the two opposite cases of no anomaly and moderate anomaly, evaluated by simulation, is shown to be over 97% for nodes and high usage trunk groups and over 90% for final trunk groups.
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