AI-driven partial topology discovery algorithm for broadband networks

Complete topology discovery is the most important MAC service in broadband networks but it holds spatial and temporal complexities for the network-wide, i.e in the data link layer it requires considerable time amount to update all link status management information. Moreover, many service providers are complaining about high operational time and resource usage in the complete topology discovery process. Additionaly, consuming high resources leads to a huge amount of management traffic on the links. At this point, partial topology discovery arises as an alternative solution as a more efficient MAC service for the next generation broadband networks to reduce complexities and maintain smooth functioning. However, manual execution of partial topology discovery leads to risky situations for the network arising from human intervention. Therefore, in this paper, we propose an AI-driven partial topology discovery approach to maintain a global MAC service which serves both physical and virtual connections in a broadband network. Besides, with this approach, we not only preserve the network resources but also have the ability of forecasting the device-based network statistics. For this aim, we use Hidden Markov Model in order to estimate the path to be discovered regarding the arrived log patterns of the devices. Thanks to the partial path estimation, we eliminate the usage of every node in the discovery and achieve up-to-date topology information more rapidly. Consequently, according to our simulations, we succeed in a significant reduction in the number of nodes used by 60%, required time to have up-to-date topology by 35%. And finally, as a consequence of using less amount of nodes, we reduce the management traffic on the links on average 50%.