Fast and scalable routing in large telecommunication networks

Fast and scalable routing in large telecommunication networks Computing the shortest path between nodes is the cornerstone of many graph algorithms and applications. It is also a constrained problem of optimization which has been studied in recent years. Typically, Traditional methods such as Dijkstra or breadth-first-search (BFS) can deliver a good solution in most cases. However, these heuristic algorithms do not meet the scalability and with the problem scale increasing, these approaches are inefficient and can consume considerable time. Hence, the methods must be found to allow scalable graph processing with significant speed. In this thesis, we proposed a machine learning based method using the message passing algorithm to find the shortest path between nodes in the large telecommunication networks. Also, we show that the suggested algorithm has linear runtime complexity which provides reasonable time for finding the shortest path in the very large network graph.