The progress of 5G mobile networks, internet of things and cloud services has raised high demands and new requirements for the capacity and reliability of optical networks. To serve the rapidly increasing number of internet service users, the technologies of optical networks are continuously evolving. The development of elastic optical networks (EON) [1
] enables network controllers to scale up or down resources in order to utilize spectrum resources efficiently [2
]. However, the EON architecture increases network complexity because of the various configurations of links and signals, which makes it more challenging to maintain the high transmission quality of a lightpath from the beginning of life (BoL) to the end of life (EoL). Since a large amount of data is transmitted in each link, even a brief disruption of traffic flows can lead to disastrous degradation [2
]. Therefore, improving the reliability of optical networks is also important.
To reach a high capacity, optical networks should better utilize network resources. In many scenarios, since a planning tool cannot accurately estimate the quality of transmissions (QoT), a high design margin is mandatory, which accounts for the difference between the planned metrics and the real value to ensure proper operations of networks [3
]. A high margin can lead to the underutilization of spectrum resources. Therefore, to build a low margin optical network to increase network capacity, a more accurate planning tool is needed to estimate the QoT prior to link deployment or reconfiguration [4
]. In this case, an accurate QoT model is essential and impairment models can improve the accuracy of the QoT model. On the other hand, to improve the reliability of optical networks, controllers should be capable of obtaining the real-time status of networks to prevent the serious degradation of systems. To achieve this, advanced optical performance monitoring (OPM) techniques are essential to enable needed functionalities to monitor the QoT and impairments. If failures occur in optical networks, the monitoring mechanisms should be capable of detecting, identifying and localizing them. Therefore, in summary, the modeling and monitoring techniques are the key building blocks for the next generation EON. The basic architecture of the modeling and monitoring techniques is shown in Figure 1
For the modeling, some models are applied to judge whether one lightpath meets the requirement for establishment in terms of the QoT [4
]. Some are applied to estimate the specific value of the QoT or impairments [5
]. In EON, there are some challenges for traditional analytical models. Firstly, there exists typically a tradeoff between complexity and accuracy. Some sophisticated analytical models, e.g., the split-step Fourier method (SSFM) [6
], are capable of capturing different impairments with great precision, but the complexity may be prohibitively high. Some approximate models, e.g., the Gaussian noise (GN) model [7
], can be calculated in a short time, but the accuracy needs to be improved especially for heterogeneous and dynamic links. Moreover, because of the diversity of EON, it is difficult to obtain one specific model for all scenarios. In this case, the estimation results of models may appear a large deviation for some scenarios.
Artificial intelligence (AI) [8
] technologies provide new opportunities to solve these problems. In many scenarios, machine learning (ML) methods can obtain a higher accuracy and/or a lower complexity compared to analytical models. For instance, in [5
], an artificial neural network (ANN) is adopted to estimate fiber nonlinear noise more accurately and efficiently compared to the original analytical model. The accuracy of this ANN-based nonlinear estimator is higher than the incoherent GN (IGN) model and the complexity is much lower than the SSFM. Moreover, for situations where there is no suitable traditional model, ML methods can make estimations utilizing the data extracted from simulations or real scenes. For example, the filtering effect brought by reconfigurable optical add-drop multiplexers (ROADM) can be modeled with an ANN [9
]. Finally, many data-driven methods with ML can be adopted to adjust analytical models to be scalable for more scenarios where they show large deviations. For instance, in [10
], ML algorithms are used to improve the performance of the analytical model with data collected from an established lightpath.
The transmission performance of an established light path is not always reliable due to the various changes of link conditions. Therefore, optical performance monitoring (OPM) is a key building block, which enables network controllers to adjust link configurations according to the real-time status of a system. Moreover, monitoring results can be used to detect, identify and localize failures in EON’s. However, the heterogeneity of EON’s has also raised many new challenging requirements for the monitoring techniques, and ML shows a potential in building more intelligent and efficient monitoring schemes. Firstly, faster response time is desired for monitoring [2
]. Since a monitoring agent should provide information for optimizing lightpath configurations and diagnosing the anomaly, the monitoring scheme needs to be capable of tracking the change of the network performance. According to [2
], the monitoring time of some network applications is required to be at the order of milliseconds. Therefore, some traditional methods with complex data processing and a long-time window may not be compatible with dynamic real-time applications. To solve this problem, advanced ML methods with forward propagation mechanisms [11
], such as ANN, convolutional neural networks (CNN) and so on, can be employed to accomplish the feature extraction and estimate real-time status in a short time period [5
]. These monitoring tools can be trained offline before deployment. When estimating the signal performance, the pre-trained monitors can respond in a very short time. Secondly, monitoring techniques should be cost-effective [2
]. In particular, they should not necessitate expensive external devices, and one OPM block is preferable to monitor multiple impairments. It may be difficult for analytical models to achieve these two goals simultaneously but ML-aided methods can help to fulfill these requirements. For instance, samples of received signals can be input to ML algorithms for monitoring the chromatic dispersion (CD), polarization-mode dispersion (PMD) and optical signal-to-noise ratio (OSNR) at the same time [14
]. Moreover, when obtaining information from the receiver digital signal processing (DSP) modules, ML methods may be able to monitor the QoT or impairments without any external devices such as the optical spectrum analyzer (OSA) [15
Therefore, for the next generation EON, applications of ML techniques for modeling and monitoring can provide strong support to build a reliable and intelligent optical network with lower design margins. This paper is intended to review recent progress in AI-enabled modeling and monitoring techniques for EON. Since optical networks are full of data with heterogeneous sources and various characteristics, it is possible to improve the accuracy and/or sensitivity of optical performance estimation functionalities with these data. However, the large number of data also makes it more challenging to discover useful information from them. In this case, data-driven ML methods are essential tools for network planning and management, but these methods should be improved to be cost-effective and reliable for deployment. Several previous review works have provided comprehensive summaries of the applications of ML techniques in optical networks [2
]. They discuss the ML-based techniques adopted in various domains and point out many possible directions for the future deployment strategies. In this paper, we focus on the AI-based techniques specifically for link modeling and monitoring in optical networks. In addition, we discuss and summarize the advantages and challenges for adopting the AI-based modeling and monitoring methods in the future EON. This paper is organized as follows.
In Section 2
, we firstly introduce the background and challenges for modeling the QoT and impairments in EON’s. The potentials of applying ML to estimate network performance are also discussed. Then, we review many previous works on ML-based modeling techniques.
In Section 3
, we firstly review various previous works on ML-based monitoring techniques. Afterwards, the monitoring techniques specifically for failure management are elaborated.
In Section 4
, the use cases for AI-based modeling and monitoring techniques are discussed.
In Section 5
, we provide a lookout for the future of utilizing ML methods in EON by discussing both the challenges and opportunities.
In Section 6
, a conclusion for this paper is provided.