A method of identifying and classifying the behavior modes of a plurality of data relative to a telephony infrastructure for Network Function Virtualization

A method for identifying and classifying the modes of behavior of a plurality of data relating to a telephone infrastructure for the virtualization of network functions comprising the steps of: providing (1) a first database containing historical monitoring data of the telephone infrastructure related to the level of use of telephone infrastructure resources; provide (2) a second database containing historical telephone infrastructure application monitoring data related to the usage level of the telephone infrastructure application; providing (3) a control unit in signal communication with the first and second databases; provide (4) a Self-Organizing Map type neural network; extrapolating (5), by the control unit, at least a first subset of historical data from the historical monitoring data of the telephone infrastructure, said first subset of data being relative to a predetermined time window; extrapolating (6), by means of the control unit, at least a second subset of historical data from the historical data monitoring the application, said second subset of historical data being relative to the predetermined time window; defining (7), by the control unit, at least one historical input vector comprising the historical data of the first and second subsets of historical data; train (8), by the control unit, the neural network to define a weight vector comprising a plurality of weight coefficients for each neuron of the neural network so as to provide at least one historical input vector as a input to the neural network, the behavioral mode of the historical data of at least one historical input vector is obtained as an output, each weight vector representing a respective behavioral mode; acquiring (9), by the control unit, a plurality of monitoring data of the current telephone infrastructure and application monitoring data of the current telephone infrastructure, said current data being acquired in real time and being relative to a window current; defining (10), by the control unit, at least one current input vector comprising the currently acquired data; providing (11), by the control unit, the at least one current input vector at the input of the trained neural network to obtain, at the output, the neuron of the neural network that is most stimulated by the vector of current entry; analyze (12) the weight vector associated with the neuron obtained in the previous step to define the behavior mode of the current data of the current input vector. (Automatic translation with Google Translate, without legal value)