Scheduling of OSPF routing table calculation using Generalized Regression Neural Network

摘要

Upon topology changes, routing protocols typically undergo convergence process to prepare new shortest routes needed for packet delivery. Real-time applications (e.g. VoIP) nowadays require routing protocol to have quick convergence time. This paper presents a new routing table calculation scheduling scheme for OSPF routing protocol to better serve real-time applications. The proposed scheme focuses on speeding up OSPF networks convergence time by optimizing the scheduling of routing table calculations using Generalized Regression Neural Network (GRNN). GRNN is used to determine the suitable hold time based on three parameters: LSA-inter arrival time, the number of important control message in queue, and the computing utilization of the routers. The GRNN model produces good hold time values for the training and testing data we used.