Topology selection criteria for a virtual topology controller based on neural memories

This work extends a previously proposed algorithm for virtual topology reconfiguration in all optical networks. Earlier, an algorithm using auto-associative neural memories has been presented. The algorithm stores topologies by assigning equal weights. In this work, we analyzed the effect of weighing topologies differently based on maximum flow, average weighted hop and the age of topology. Although we focus on optical networks, the algorithm and the analysis we present here can be useful in other network domains, such as wireless networks.