Machine Learning for Multi-Layer Open and Disaggregated Optical Networks

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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

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

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

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

Knowledge Distillation-Based Compression Model for QoT Estimation of an Unestablished Lightpaths

A precise Quality-of-transmission (QoT) estimation of a Lightpath (LP) before its deployment is a key step in effective network design and resource utilization. Deep neural network-based methods have recently shown promising results for QoT estimation tasks. However, these methods contain a large number of parameters and require heavy computational resources for accurate predictions. To this end, we propose a novel Knowledge distillation (KD) based compression method to obtain a compact and more accurate model for QoT estimation. Our simulation results demonstrate that the model trained using KD significantly improves accuracy with reduced parameters and computational complexity. To the best of our knowledge, this is the first time that the knowledge distillation technique has been used to estimate the QoT of an unestablished LP

Query Based Iterative Learning Approach for Lightpath Deployment in Optical Networks

Predicting the Quality of Transmission (QoT) of a Lightpath (LP) before its actual deployment is important for efficient resource utilization. Conventionally, analytical models using closed-loop formulation estimate QoT, which imposes substantial margins to avoid network outages. Recently, data-driven techniques have been shown as a potential alternative with excellent precision and real-time applicability. However, data-driven techniques require sufficient training data, which might be challenging to acquire during real network operations. In this context, we proposed a novel unsupervised Iterative learning (IL) framework developed on top of the Random forest (RF) classifier for QoT estimation of LP before deployment. We considered the Generalized signal-to-noise ratio (GSNR) as a characterizing parameter for QoT estimation of LP. Our simulation results illustrate that, by employing the proposed iterative learning approach, we can obtain 99% classification accuracy with a reduced number of training samples compared to the traditional supervised learning approach

Smart Provisioning of Sliceable Bandwidth Variable Transponders in Elastic Optical Networks

Prior provisioning of optical source technologies have techno-economic importance for the operator during the design and planning of optical network architectonics. Advancement towards the latest technology paradigm such as Elastic Optical Networks (EONs) and Software Defined Networking (SDN) open a gateway for a flexible and re-configurable optical network architecture. In order to achieve the required degree of flexibility, a flexible and dynamic behaviour is required both at the control and data plane. In this regards, SDN-enabled flexible optical transceivers are proposed to provide the required degree of flexibility. Sliceable Bandwidth Variable Transponders (SBVTs) is one of the recent type of flexible optical transceivers. Based on the type/technology of optical carrier source, the SBVTs are categorized into two types; Multi-Laser SBVT (ML-SBVT) and Multi-wavelength SBVT (MW-SBVT). Both architectures have their own pros and cons when it comes to accommodate traffic request. In this paper, we propose a selection model for the SBVTs before its actual deployment in the network. The selection model consider various design and planning phase network characteristics. In addition to this selection model, the comparison of centralized Flex-OCSM architecture is also presented with the already discussed SBVT types. The analysis in this work is performed on random network (20 nodes) and the German Network (17 nodes)

Iterative Transfer Learning Approach for QoT Prediction of Lightpath in Optical Networks

Machine learning (ML) has been widely used in optical networks for accurate Quality-of-transmission (QoT) estimation of Lightpaths (LPs). However, this domain has two main issues: ML-based models require a sufficiently large amount of data for training, and once the model is trained on one type of configuration, it cannot be used for another configuration. This paper focuses on these two issues and proposes an Active Transfer Learning (ATL) based solution. In ATL, Active learning (AL) helps in reducing the dataset’s size while not compromising the model’s performance, while the Transfer learning (TL) concept enables the transfer of knowledge from a source domain to the target domain with improved accuracy. This combined approach of ATL delivers promising results with minimum data samples and enhanced performance