A deep neural network with a fuzzy multi-objective optimization model for fault analysis in an elastic optical network

Abstract

The elastic optical network (EON) is the most attractive architecture for the next generation of optical networks. Dealing with high bit-rate traffic, EON faces the challenge of ensuring survivability to operate with stringent Service Level Agreements. This article proposes a Deep Neural Network model with a multi-objective Fuzzy Inference System (FIS) to solve the Routing and Spectrum Assignment problem with Shared Backup Path Protection. The algorithm aims to optimize the trade-off between blocking probability (BP) and fault restoration ratio (FRR). It uses a new spectrum-fragmentation metric to improve the FRR of affected connections. The FIS adds features of load balancing and alignment of allocation path solutions. We use figures of merit as BP of connection requests, FRR, spectrum utilization ratio, and connection downtime to evaluate the algorithm performance. The proposed algorithm organizes traffic in a less fragmented way, efficiently uses routing and protection resources, and performs well compared to similar algorithms related in the literature.