Analyzing surrogate models to assess Blocking Probability of optical networks

Recent studies demonstrated the feasibility of surrogate methods to assess optical networks based on Artificial Neural Networks (ANNs). However, surrogate methods present different trade offs between accuracy and resource utilization efficiency, such as computational time. In this paper we analyze the use of ANN to forecast the Blocking Probability (BP) of deployed optical networks considering different architectures for the underlying alternative method. We also analyze the impact of the adopted physical layer model and the number of optical networks needed to train the ANN. We compare the results of our proposal with the outcome of a discrete event network simulator. From our results we can conclude that ANN is a promising technique to estimate the BP of transparent optical networks, but the dataset used to train the ANN and the physical layer model are crucial for the proper design of this type of tool.