Loss estimation and control mechanism in bufferless optical packet-switched networks based on multilayer perceptron

Abstract

Artificial neural networks (ANNs) are well-known estimators for the output of broad range of complex systems and functions. In this paper, a common ANN architecture called multilayer perceptron (MLP) is used as a fast optical packet loss rate (OPLR) estimator for bufferless optical packet-switched (OPS) networks. Considering average loads at the ingress switches of an OPS network, the proposed estimator estimates total OPLR as well as ingress OPLRs (the OPLR of optical packets sent from individual ingress switches). Moreover, a traffic policing algorithm called OPLRC is proposed to control ingress OPLRs in bufferless slotted OPS networks with asymmetric loads. OPLRC is a centralized greedy algorithm which uses estimated ingress OPLRs of a trained MLP to tag some optical packets at the ingress switches as eligible for drop at the core switches in case of contention. This will control ingress OPLRs of un-tagged optical packets within the specified limits while giving some chance for tagged optical packets to reach their destinations. Eventually, the accuracy of the proposed estimator along with the performance of the proposed algorithm is evaluated by extensive simulations. In terms of the algorithm, the results show that OPLRC is capable of controlling ingress OPLRs of un-tagged optical packets with an acceptable accuracy.