An Intelligent Routing Frame Work for High Traffic Networks using Deep Learning

The advent of internet usage and technological advancements has resulted in the exponential growth of network traffic, further aggravating this problem by presenting significant challenges in multiple aspects, from managing a network to routing. Traditional routing cannot handle these very high-traffic networks, which causes bottlenecks and network congestion. In this paper, we present a deep learning-based intelligent routing model to solve the above problem. In this context, a deep learning algorithm is applied to analyze the network traffic patterns and predict the routing of data packets assembled at other stations on the fly on the fly. It even dynamically modifies routing decisions, considering network topology, link capacity, and traffic load. It could be continuous training of the deep learning model with fresh data or online updates to a pre-trained model so that shifts in network conditions can be continuously accounted for. We also allow the deep learning model to have some form of memory and use it to learn from its past decisions. Additionally, the framework will come equipped with a network monitoring system to gather and filter data to discover potential network problems and proactively take routing decision changes.