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.
Proceedings of the IFAC Symposium on System Identification 2018
computer networks; identification; Kalman filter; state space models; traffic estimation; Control and Systems Engineering
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1085154
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