SOON: self-optimizing optical networks with machine learning

As optical networks undergo rapid development, the trade-offs among higher network service capability, and increasing operating expense (OPEX) about operations, administration and maintenance (OAM) become telecom operators' key obstacles. Intelligent and automatic OAM is considered to effectively satisfy service requirements, while dampening OPEX growth. In particular, machine learning (ML) has been investigated as a possible method of replacing human image recognition, nature language processing, automatic drive, and so forth. This is because of its essential feature extraction ability. ML application in optical networks was studied in a preliminary way recently. In ML-enabled optical networks, huge data storage and powerful computing resources are required to handle computer-intensive tasks performed in order to analyze features from big data sets. Integration of these two key resources into existing optical network architectures, in order to improve network performance, is an emerging challenge for ML-enabled optical networks. This article proposes a novel optical network architecture, which is based on software-defined networking (SDN), which is also named self-optimizing optical networks (SOON). First, we comb through intelligence development of optical networks, and introduce SOON as an OAM-oriented optical network architecture. Second, we demonstrate four typical applications within SOON, including tidal traffic prediction, alarm prediction, anomaly action detection, and routing and wavelength assignment. Finally, we discuss some open issues.