In this paper, data-based estimation of traffic flows in computer networks is dealt with. The knowledge of the traffic matrix, containing the values of flows among the nodes in a computer network, could be very useful in many aspects of network management, like capacity planning, traffic load distribution and anomaly detection. The proposed technique exploits a new observer based on Kalman filtering, suitably modified to better fit into the specific application framework. Experimental results on a Metropolitan Area Network in Milan (Italy) show that the proposed method may obtain very low error rates even in presence of severe technological constraints.
A Kalman filtering approach to traffic flow estimation in computer networks
Formentin, S.;Bittanti, S.
2018-01-01
Abstract
In this paper, data-based estimation of traffic flows in computer networks is dealt with. The knowledge of the traffic matrix, containing the values of flows among the nodes in a computer network, could be very useful in many aspects of network management, like capacity planning, traffic load distribution and anomaly detection. The proposed technique exploits a new observer based on Kalman filtering, suitably modified to better fit into the specific application framework. Experimental results on a Metropolitan Area Network in Milan (Italy) show that the proposed method may obtain very low error rates even in presence of severe technological constraints.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.