Evaluating Cross- feature Trained Machine Learning Models for Estimating QoT of Unestablished Lightpaths

The rapid increase in bandwidth-driven applications has resulted in exponential internet traffic growth, especially in the backbone networks. To address this growth of internet traffic, operators always demand the total capacity utilization of underlying infrastructure. In this perspective, precise estimation of the quality of transmission (QoT) of the lightpaths (LPs) is vital for reducing the margins provisioned by uncertainty in network equipment's working point. This article proposes and compares several data-driven Machine learning (ML) based models to estimate QoT of unestablished LP before its deployment in the future deploying network. The proposed models are cross-trained on the data acquired from an already established LP of an entirely different in-service network. The metric considered to evaluate the QoT of LP is the Generalized Signal-to-Noise Ratio (GSNR). The dataset is generated synthetically using well tested GNPy simulation tool. Promising results are achieved to reduce the GSNR uncertainty and, consequently, the provisioning margin

Experimental demonstration of cognitive provisioning and alien wavelength monitoring in multi-domain EON

This paper proposes a cognitive multi-domain EON architecture with machine-learning aided RMSA and alien wavelength monitoring. Testbed experiments show modulation format recognition, QoT monitoring and cognitive routing for a 160 GBd alien multi-wavelength lightpath.Peer ReviewedPostprint (published version

Experimental demonstration of machine-learning-aided QoT estimation in multi-domain elastic optical networks with alien wavelengths

In multi-domain elastic optical networks with alien wavelengths, each domain needs to consider intradomain and interdomain alien traffic to estimate and guarantee the required quality of transmission (QoT) for each lightpath and perform provisioning operations. This paper experimentally demonstrates an alien wavelength performance monitoring technique and machine-learning-aided QoT estimation for lightpath provisioning of intradomain/interdomain traffic. Testbed experiments demonstrate modulation format recognition, QoT monitoring, and cognitive routing for a 160 Gbaud alien multi-wavelength lightpath. By using experimental training datasets from the testbed and an artificial neural network, we demonstrated an accurate optical-signal-to-noise ratio prediction with an accuracy of ~95% when using 1200 data points.Peer ReviewedPostprint (author's final draft

Advanced Formulation of QoT-Estimation for Un-established Lightpaths Using Cross-train Machine Learning Methods

Planning tools with excellent accuracy along with precise and advance estimation of the quality of transmission (QoT) of lightpaths (LPs) have techno-economic importance for a network operator. The QoT metric of LPs is defined by the generalized signal-to-noise ratio (GSNR) which includes the effect of both amplified spontaneous emission (ASE) noise and non-linear interference (NLI) accumulation. Typically, a considerable number of analytical models are available for the estimation of QoT but all of them require the exact description of system parameters. Thus, the analytical models are impractical in case of un-used network scenarios. In this study, we exploit an alternative approach based on three machine learning (ML) techniques for QoT estimation (QoT-E). The proposed ML based techniques are cross-trained on the characteristic features extracted from the telemetry data of the already in-service network. This new approach provides a reliable QoT-E and consequently assists the network operator in network planning and also enables the reliable low-margin LP deployment

QoT Estimation for Light-path Provisioning in Un-Seen Optical Networks using Machine Learning

We propose the use of machine-learning based regression model to predict the quality of transmission (QoT) of an un-established lightpath (LP) in an un-seen network prior to its actual deployment, based on telemetry data of already established LPs of different network. This advance prediction of the QoT of un-established LP in an un-seen network has a promising factor not only for the optimal designing of this network but also enables the possibility to automatically deploy the LPs with a minimum margin in a reliable manner. The QoT metric of the LPs are defined by the Generalized Signal-to-Noise Ratio (GSNR) which includes the effect of both Amplified Spontaneous Emission (ASE) noise and Non-Linear Interference (NLI) accumulation. In the response of present simulation scenario, the real field telemetry data is mimicked by using a well reliable and tested network simulation tool GNPy. Using the generated data set, a machine-learning technique is applied, demonstrating the GSNR prediction of an un-established LP in an unrevealed network with maximum error of 0.53 dB

Cross-feature trained machine learning models for QoT-estimation in optical networks

The ever-increasing demand for global internet traffic, together with evolving concepts of software-defined networks and elastic-optical-networks, demand not only the total capacity utilization of underlying infrastructure but also a dynamic, flexible, and transparent optical network. In general, worst-case assumptions are utilized to calculate the quality of transmission (QoT) with provisioning of high-margin requirements. Thus, precise estimation of the QoT for the lightpath (LP) establishment is crucial for reducing the provisioning margins. We propose and compare several data-driven machine learning (ML) models to make an accurate calculation of the QoT before the actual establishment of the LP in an unseen network. The proposed models are trained on the data acquired from an already established LP of a completely different network. The metric considered to evaluate the QoT of the LP is the generalized signal-to-noise ratio (GSNR), which accumulates the impact of both nonlinear interference and amplified spontaneous emission noise. The dataset is generated synthetically using a well-tested GNPy simulation tool. Promising results are achieved, showing that the proposed neural network considerably minimizes the GSNR uncertainty and, consequently, the provisioning margin. Furthermore, we also analyze the impact of cross-features and relevant features training on the proposed ML models’ performance

Experimental demonstration of machine-learning-aided QoT estimation in multi-domain elastic optical networks with alien wavelengths

In multi-domain elastic optical networks with alien wavelengths, each domain needs to consider intradomain and interdomain alien traffic to estimate and guarantee the required quality of transmission (QoT) for each lightpath and perform provisioning operations. This paper experimentally demonstrates an alien wavelength performance monitoring technique and machine-learning-aided QoT estimation for lightpath provisioning of intradomain/interdomain traffic. Testbed experiments demonstrate modulation format recognition, QoT monitoring, and cognitive routing for a 160 Gbaud alien multi-wavelength lightpath. By using experimental training datasets from the testbed and an artificial neural network, we demonstrated an accurate optical-signal-to-noise ratio prediction with an accuracy of ∼95% when using 1200 data points

Cross-Train: Machine Learning Assisted QoT-Estimation in Un-used Optical Networks

The quality of transmission (QoT) estimation of lightpaths (LPs) has both technological and economic significance from the operator’s perspective. TThe quality of transmission (QoT) estimation of lightpaths (LPs) has both technological and economic significance from the operator’s perspective. Typically, the network administrator configures the network element (NE) working point according to the specified nominal values given by vendors. These operational NEs experienced some variation from the given nominal working point and thus put up uncertainty during their operation, resulting in the introduction of uncertainty in estimating LP QoT. Consequently, a substantial margin is required to avoid any network outage. In this context, to reduce the required margin provisioning, a machine learning (ML) based framework is proposed which is cross-trained using the information retrieved from the fully operational network and utilized to support the QoT estimation unit of an un-used sister network

Performance studies of evolutionary transfer learning for end-to-end QoT estimation in multi-domain optical networks [Invited]

This paper proposes an evolutionary transfer learning approach (Evol-TL) for scalable quality-of-transmission (QoT) estimation in multi-domain elastic optical networks (MD-EONs). Evol-TL exploits a broker-based MD-EON architecture that enables cooperative learning between the broker plane (end-to-end) and domain-level (local) machine learning functions while securing the autonomy of each domain. We designed a genetic algorithm to optimize the neural network architectures and the sets of weights to be transferred between the source and destination tasks. We evaluated the performance of Evol-TL with three case studies considering the QoT estimation task for lightpaths with (i) different path lengths (in terms of the numbers of fiber links traversed), (ii) different modulation formats, and (iii) different device conditions (emulated by introducing different levels of wavelength-specific attenuation to the amplifiers). The results show that the proposed approach can reduce the average amount of required training data by up to 13× while achieving an estimation accuracy above 95%

Assessment of Cross-train Machine Learning Techniques for QoT-Estimation in agnostic Optical Networks

With the evolution of 5G technology, high definition video, virtual reality, and the internet of things (IoT), the demand for high capacity optical networks has been increasing dramatically. To support the capacity demand, low-margin optical networks engage operator interest. To engross this techno-economic interest, planning tools with higher accuracy and accurate models for the quality of transmission estimation (QoT-E) are needed. However, considering the state-of-the-art optical network’s heterogeneity, it is challenging to develop such an accurate planning tool and low-margin QoT-E models using the traditional analytical approach. Fortunately, data-driven machine-learning (ML) cognition provides a promising path. This paper reports the use of cross-trained ML-based learning methods to predict the QoT of an un-established lightpath (LP) in an agnostic network based on the retrieved data from already established LPs of an in-service network. This advanced prediction of the QoT of un-established LP in an agnostic network is a key enabler not only for the optimal planning of this network but it also provides the opportunity to automatically deploy the LPs with a minimum margin in a reliable manner. The QoT metric of the LPs are defined by the generalized signal-to-noise ratio (GSNR), which includes the effect of both amplified spontaneous emission (ASE) noise and non-linear interference (NLI) accumulation. The real field data is mimicked by using a well reliable and tested network simulation tool GNPy. Using the generated synthetic data set, supervised ML techniques such as wide deep neural network, deep neural network, multi-layer perceptron regressor, boasted tree regressor, decision tree regressor, and random forest regressor are applied, demonstrating the GSNR prediction of an un-established LP in an agnostic network with a maximum error of 0.40 dB