BER Degradation Detection and Failure Identification in Elastic Optical Networks

Optical connections support virtual links in MPLS-over-optical multilayer networks and therefore, errors in the optical layer impact on the quality of the services deployed on such networks. Monitoring the performance of the physical layer allows verifying the proper operation of optical connections, as well as detecting bit error rate (BER) degradations and anticipating connection disruption. In addition, failure identification facilitates localizing the cause of the failure by providing a short list of potential failed elements and enables self-decision making to keep committed service level. In this paper, we analyze several failure causes affecting the quality of optical connections and propose two different algorithms: one focused on detecting significant BER changes in optical connections, named as BANDO, and the other focused on identifying the most probable failure pattern, named as LUCIDA. BANDO runs inside the network nodes to accelerate degradation detection and sends a notification to the LUCIDA algorithm running on the centralized controller. Experimental measures were carried out on two different setups to obtain values for BER and received power and used to generate synthetic data used in subsequent simulations. Results show significant improvement anticipating maximum BER violation with small failure identification errors.