Artificial Intelligence Control Logic in Next-Generation Programmable Networks

Żotkiewicz, Mateusz; Szałyga, Wiktor; Domaszewicz, J.; Bak, Andrzej; Kopertowski, Zbigniew; Kozdrowski, Stanisław

doi:10.3390/app11199163

Cited by 3 publications

(4 citation statements)

References 28 publications

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“…SDN and knowledge-based networking (KDN) paradigms are instrumental in implementing frequent network reconfiguration and reoptimization within a DWDM network [9,10]. Central to SDN and KDN is an accurate estimation of transmission quality [11,12].…”

Section: Related Workmentioning

confidence: 99%

“…Here, we follow an alternative approach to the application of ML to the automatic reconfiguration of an optical network that relies only on the data that are available to a network operator via the control plane. Such an approach is distinct from the one followed in [10,13]. Similar lightpath QoT estimation problems were also addressed in [20,21], whereby the solution presented there mainly focused on an application of the SVM classifier; meanwhile, in [22], the authors used the random forest algorithm.…”

Section: Related Workmentioning

confidence: 99%

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A Comprehensive Study of Machine Learning Application to Transmission Quality Assessment in Optical Networks

et al. 2023

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This paper examines applying machine learning to the assessment of the quality of the transmission in optical networks. The motivation for research into this problem derives from the fact that the accurate assessment of transmission quality is key to an effective management of an optical network by a network operator. In order to facilitate a potential implementation of the proposed solution by a network operator, the training data for the machine learning algorithms are directly extracted from an operating network via a control plane. Particularly, this work focuses on the application of single class and binary classification machine learning algorithms to optical network transmission quality assessment. The results obtained show that the best performance can be achieved using gradient boosting and random forest algorithms.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Comprehensive Study of Machine Learning Application to Transmission Quality Assessment in Optical Networks

et al. 2023

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“…Frequent network reconfiguration and re-optimization necessary to make the best use of available resources has been facilitated by the introduction of software-defined networking (SDN) and knowledge-based networking (KDN) paradigms [7,[9][10][11]. Central to SDN and KDN is automatic provisioning of optical channels (lightpaths), which is based on accurate quality estimation for them.…”

Section: Introductionmentioning

confidence: 99%

“…However, in the approach presented here, we apply ML to a database that has been extracted directly via the control plane from the DWDM network under analysis. This approach leads to an ML problem that is clearly different from the one addressed in [6,7,11,12], as there are significant challenges in using real optical network datasets, related to data representation, data size and class imbalance, which is intrinsic to data gathered via control plane from an operating DWDM network. The class imbalance follows from the fact that in an operating DWDM network there may be dozens or hundreds of operating connections but there is not much information available (if any) on connections that could not be realized due to excessive bit error rate.…”

Section: Introductionmentioning

confidence: 99%

Learning to Classify DWDM Optical Channels from Tiny and Imbalanced Data

Cichosz

Kozdrowski

Sujecki

2021

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Applying machine learning algorithms for assessing the transmission quality in optical networks is associated with substantial challenges. Datasets that could provide training instances tend to be small and heavily imbalanced. This requires applying imbalanced compensation techniques when using binary classification algorithms, but it also makes one-class classification, learning only from instances of the majority class, a noteworthy alternative. This work examines the utility of both these approaches using a real dataset from a Dense Wavelength Division Multiplexing network operator, gathered through the network control plane. The dataset is indeed of a very small size and contains very few examples of “bad” paths that do not deliver the required level of transmission quality. Two binary classification algorithms, random forest and extreme gradient boosting, are used in combination with two imbalance handling methods, instance weighting and synthetic minority class instance generation. Their predictive performance is compared with that of four one-class classification algorithms: One-class SVM, one-class naive Bayes classifier, isolation forest, and maximum entropy modeling. The one-class approach turns out to be clearly superior, particularly with respect to the level of classification precision, making it possible to obtain more practically useful models.

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