Network Routing Optimization Based on Machine Learning Using Graph Networks Robust against Topology Change
There is an increasing demand of real time routing optimazation using Sotware Defined Networking (SDN) for better Quality of Service. Since the problem of finding the optimum routing for any QoS metric is hard to solve for a medium or larger size network, quasi optimization using metaheuristics, such as Genetic Algorithm (GA) and Simulated Annealing (SA) have been investigated, but it is still impossible to satisfy the requirement of real time optimization. There have been some attempts to solve this with machine learning. By learning a model beforehand, it is possible to output a near optimal solution in a short time during network operation. The open problem with this approach is that machine learning models cannot deal with topology change of the network. In this paper, we create a model which is robust for topology change by using Graph Networks. Applying the proposed model to maximum bandwidth utilization, we have gotten the accuracy of about 61.0% for solution of GA, and the prediction time is 150 times faster than GA.
2020 International Conference on Information Networking (ICOIN)