Experimental Demonstration of Machine-Learning-Aided QoT Estimation in Multi-Domain Elastic Optical Networks with Alien Wavelengths

Proietti, Roberto; Chen, Xiaoliang; Zhang, Kaiqi; Liu, Gengchen; Shamsabardeh, M.; Castro, Alberto; Velasco, Luis; Zhu, Zuqing; Yoo, S. J. B.

doi:10.1364/jocn.11.0000a1

Cited by 58 publications

(28 citation statements)

References 25 publications

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“…Recent works in [63]- [65] have used ANN, SVM, and Gaussian process regressions, respectively, to predict the OSNR in an optical network. The predicted OSNR for each source-todestination path are then used by the system to determine the best path for the incoming traffic.…”

Section: A Predicting the Network Optical Signal To Noise Ratiomentioning

confidence: 99%

Applications of Machine Learning in Networking: A Survey of Current Issues and Future Challenges

et al. 2021

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Communication networks are expanding rapidly and becoming increasingly complex. As a consequence, the conventional rule-based algorithms or protocols may no longer perform at their best efficiencies in these networks. Machine learning (ML) has recently been applied to solve complex problems in many fields, including finance, health care, and business. ML algorithms can offer computational models that can solve complex communication network problems and consequently improve performance. This paper reviews the recent trends in the application of ML models in communication networks for prediction, intrusion detection, route and path assignment, Quality of Service improvement, and resource management. A review of the recent literature reveals extensive opportunities for researchers to exploit the advantages of ML in solving complex performance issues in a network, especially with the advancement of softwaredefined networks and 5G.

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Section: A Predicting the Network Optical Signal To Noise Ratiomentioning

confidence: 99%

Applications of Machine Learning in Networking: A Survey of Current Issues and Future Challenges

et al. 2021

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“…To emulate failure scenarios, we modified the characteristics of the 2nd WSS of every node in the setup; its bandwidth and central frequency were modified to model FT and FS failures, respectively. A large dataset of failures was collected by inducing failures of magnitude in the range [1][2][3][4][5][6][7][8] GHz for FS and in the range [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15] GHz for FT. We configured optical filters to be 2nd order Gaussian for training and reconfigured them to become 3rd and 4th order Gaussian for testing, where the same failure scenarios were simulated.…”

Section: B Out-of-field ML Training With In-field Adaptationmentioning

confidence: 99%

“…Behind the autonomic concept, machine learning (ML) plays an essential role for a wide range of use cases in optical networks (see [3][4][5][6][7]). Examples include use cases from self-configuration to predictive maintenance and, at several levels, from transmission to single and multilayer network [8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

“…Note that ML training makes use of known output feature(s) to derive a model relating input and output data. However, existing legal and regulatory context limits the availability of real network performance measurement, and few research studies can be found where the data came from real measurements (see [12,14]). Moreover, obtaining training datasets belonging to specific pre-commercial and commercial technologies, as well as current and forecasted scenarios is a complex and extremely difficult task due to the vast combination of potential cases for the application of ML models.…”

Section: Introductionmentioning

confidence: 99%

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Learning Life Cycle to Speed Up Autonomic Optical Transmission and Networking Adoption

Velasco¹,

Shariati²,

Boitier

et al. 2019

J. Opt. Commun. Netw.

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Autonomic optical transmission and networking requires machine learning (ML) models to be trained with large datasets. However, the availability of enough real data to produce accurate ML models is rarely ensured since new optical equipment and techniques are continuously being deployed in the network. One option is to generate data from simulations and lab experiments, but such data could not cover the whole features space and would translate into inaccuracies in the ML models. In this paper, we propose an ML-based algorithm life cycle to facilitate ML deployment in real operator networks. The dataset for ML training can be initially populated based on the results from simulations and lab experiments. Once ML models are generated, ML retraining can be performed after inaccuracies are detected to improve their precision. Illustrative numerical results show the benefits of the proposed learning cycle for general use cases. In addition, two specific use cases are proposed and demonstrated that implement different learning strategies: (i) a two-phase strategy performing out-of-field training using data from simulations and lab experiments with generic equipment, followed by an in-field adaptation to support heterogeneous equipment (the accuracy of this strategy is shown for a use case of failure detection and identification), and (ii) in-field retraining, where ML models are retrained after detecting model inaccuracies. Different approaches are analyzed and evaluated for a use case of autonomic transmission, where results show the significant benefits of collective learning.

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“…AI in the optimal network not only improves the utilization of the wavelength but also improve management efficiency. Proietti et al utilize machine learning-aided Quality of Transmission (QoT) estimation for lightpath configuration of intra-inter-domain traffic and obtain high accurate Optical Signal to Noise (OSNR) prediction [3]. Hagos et al present a robust, scalable and generic machine learning-based method which may be of interest for network operators that experimentally infers congestion window and the underlying variant of loss-based TCP algorithms within flows from passive traffic measurements collected at an intermediate node [4].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Measurement Method for Flow Traffic in Software Defined Networking

Huo¹,

Jiang

Zhu

et al. 2019

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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In Software Defined Networking (SDN), the fine-grained measurements are crucial for network management and design. However, the measurement overhead and accuracy are contradiction, how to accurately measure the network traffic with low overhead has become a hot topic. Artificial Intelligence (AI) has been used to predict the traffic in networks. Then, we propose an AI-based Lightweight Adaptive Measurement Method (ALAMM) for traffic measurement in SDN with low overhead and high measurement accuracy. Firstly, we use measurements in the front to train the AI-based traffic prediction model and utilize the model to predict traffic in SDN. Then, we obtain the sequence of sampling points by judging the change of traffic prediction and send the measurement primitive to switches to obtain coarse-grained measurements. At last, we utilize the interpolation theory to fill the coarse-grained measurement and propose an optimization function to optimize the fine-grained measurement. Simulation results show that the ALAMM is feasible, and the measurement overhead of ALAMM is low.

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Experimental Demonstration of Machine-Learning-Aided QoT Estimation in Multi-Domain Elastic Optical Networks with Alien Wavelengths

Cited by 58 publications

References 25 publications

Applications of Machine Learning in Networking: A Survey of Current Issues and Future Challenges

Applications of Machine Learning in Networking: A Survey of Current Issues and Future Challenges

Learning Life Cycle to Speed Up Autonomic Optical Transmission and Networking Adoption

An Adaptive Measurement Method for Flow Traffic in Software Defined Networking

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