Machine Learning for QoT Estimation of Unseen Optical Network States

Panayiotou, Tania; Savva, Giannis; Shariati, Behnam; Tomkos, Ioannis; Ellinas, Georgios

doi:10.1364/ofc.2019.tu2e.2

Cited by 28 publications

(17 citation statements)

References 7 publications

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“…Approaches from the above categories have been applied to various optical networking tasks, such as performance prediction and fault diagnosis. Optical path performance prediction is typically addressed with different SL models, as in [15], where the contribution of different features to the regression error is analysed, [16], where a detailed comparison to existing theoretical models is performed, and [17], where the impact of the new lightpath to existing network connections is also taken into account.…”

Section: A Autonomous Optical Network Managementmentioning

confidence: 99%

Machine Learning for Optical Network Security Monitoring: A Practical Perspective

Furdek

Natalino

Lipp³

et al. 2020

J. Lightwave Technol.

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In order to accomplish cost-efficient management of complex optical communication networks, operators are seeking automation of network diagnosis and management by means of Machine Learning (ML). To support these objectives, new functions are needed to enable cognitive, autonomous management of optical network security. This paper focuses on the challenges related to the performance of ML-based approaches for detection and localization of optical-layer attacks, and to their integration with standard Network Management Systems (NMSs). We propose a framework for cognitive security diagnostics that comprises an attack detection module with Supervised Learning (SL), Semi-Supervised Learning (SSL) and Unsupervised Learning (UL) approaches, and an attack localization module that deduces the location of a harmful connection and/or a breached link. The influence of false positives and false negatives is addressed by a newly proposed Window-based Attack Detection (WAD) approach. We provide practical implementation guidelines for the integration of the framework into the NMS and evaluate its performance in an experimental network testbed subjected to attacks, resulting with the largest optical-layer security experimental dataset reported to date.

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Section: A Autonomous Optical Network Managementmentioning

confidence: 99%

Machine Learning for Optical Network Security Monitoring: A Practical Perspective

Furdek

Natalino

Lipp³

et al. 2020

J. Lightwave Technol.

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“…Therefore, this model can enable controllers to find the optimum configuration of a light path for each specific network. In [46], a deep graph convolutional neural network (DGCNN) is applied to estimate the feasibility of the network state. This work considers the crosstalk between unestablished and established light paths according to historical data.…”

Section: Ai-based Qot Modelingmentioning

confidence: 99%

AI-Based Modeling and Monitoring Techniques for Future Intelligent Elastic Optical Networks

Liu

Lun

et al. 2020

Applied Sciences

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With the development of 5G technology, high definition video and internet of things, the capacity demand for optical networks has been increasing dramatically. To fulfill the capacity demand, low-margin optical network is attracting attentions. Therefore, planning tools with higher accuracy are needed and accurate models for quality of transmission (QoT) and impairments are the key elements to achieve this. Moreover, since the margin is low, maintaining the reliability of the optical network is also essential and optical performance monitoring (OPM) is desired. With OPM, controllers can adapt the configuration of the physical layer and detect anomalies. However, considering the heterogeneity of the modern optical network, it is difficult to build such accurate modeling and monitoring tools using traditional analytical methods. Fortunately, data-driven artificial intelligence (AI) provides a promising path. In this paper, we firstly discuss the requirements for adopting AI approaches in optical networks. Then, we review various recent progress of AI-based QoT/impairments modeling and monitoring schemes. We categorize these proposed methods by their functions and summarize advantages and challenges of adopting AI methods for these tasks. We discuss the problems remained for deploying AI-based methods to a practical system and present some possible directions for future investigation.

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“…The binary classifier is trained for estimating whether the BER of the candidate lightpath is above or below the threshold. Thus, the QoT model is capable of classifying the unestablished lightpaths into one of two classes: the infeasible QoT class or the feasible QoT class [22][23][24]. However, the practice of considering a single BER requirement may lead to connection overprovisioning, especially as the diversity of the various BER requirements increases.…”

Section: Related Workmentioning

confidence: 99%

“…This is especially true in current networks, where network planning functions are becoming increasingly complex in an uncertain network environment that is continuously changing, supporting heterogeneous applications and services. Existing ML applications [9,10] focus on traffic demand predictions and resource allocation optimization [11][12][13][14][15], fault detection/localization [16][17][18][19], attack detection/identification [20,21], and quality-of-transmission (QoT) estimation [22][23][24][25][26]. In most of these works, however, the diverse optical service level agreements (OSLAs) of the next generation optical networks [27] are not specifically considered.…”

mentioning

confidence: 99%

Decentralizing machine-learning-based QoT estimation for sliceable optical networks

Panayiotou

Savva

Tomkos

et al. 2020

J. Opt. Commun. Netw.

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Dynamic network slicing has emerged as a promising and fundamental framework for meeting 5G's diverse use cases. As machine learning (ML) is expected to play a pivotal role in the efficient control and management of these networks, in this work, we examine the ML-based quality-of-transmission (QoT) estimation problem under the dynamic network slicing context, where each slice has to meet a different QoT requirement. Specifically, we examine ML-based QoT frameworks with the aim of finding QoT model/s that are fine-tuned according to the diverse QoT requirements. Centralized and distributed frameworks are examined and compared according to their model accuracy, routing and spectrum allocation (RSA) accuracy, and CPU (training time) and RAM (memory) requirements. We show that the distributed QoT models outperform the centralized QoT model in accuracy and CPU usage. The RSA accuracy, i.e., measuring the accuracy of the models with regard to the QoT-aware RSA decisions, is sufficiently high for both frameworks. Regarding the RAM usage, as the distributed framework has to train in parallel several QoT models, it may require higher memory, especially as the number of diverse QoT requirements increases. This memory, however, tends to be reserved for a shorter period of time. Moreover, this work develops a dynamic multi-slice QoT-aware (RSA) framework that integrates the ML-based QoT models. The aim is to examine the network performance when the diverse QoT models are considered, as opposed to the state-of-the-art single-slice QoT-aware RSA approach where all connections/slices are provisioned according to a single QoT requirement. We show that the multi-slice QoT-aware RSA approach significantly improves network performance, a clear indicator that the commonly considered single-slice QoT-aware RSA approach may lead to connection overprovisioning.few years adopted network slicing as an emerging technology and business model [2,3].In general, the implementation of network slicing is conceived as an end-to-end feature spanning over different technology domains. While relevant work focuses mostly on the implementation of network slicing in the radio access network (RAN) segment [1,2,4,5], recent work also focuses on extending the scope of network slicing beyond the RAN to include both mobile and transport (optical) network segments [6][7][8]. In particular, recent work deals with SDN/NFV demonstrations, network orchestration implementations, and traffic demand analysis, towards the vision of end-to-end network slicing. In general, SDN/NFV, network orchestration, and analytics are considered as the key ingredients for successful implementation of end-to-end network slicing [1]. However, regarding the efficient control and management of these slices (i.e., enabling not only their creation but also their 1943-0620/20/070146-17 Journal smart deployment at the right place, at the right time, with optimized resources), little has been done so far.Without doubt, machine learning (ML) will be instrumental in the efficient control an...

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Machine Learning for QoT Estimation of Unseen Optical Network States

Abstract: We apply deep graph convolutional neural networks for Quality-of-Transmission estimation of unseen network states capturing, apart from other important impairments, the inter-core crosstalk that is prominent in optical networks operating with multicore fibers.

Cited by 28 publications

References 7 publications

Machine Learning for Optical Network Security Monitoring: A Practical Perspective

Machine Learning for Optical Network Security Monitoring: A Practical Perspective

AI-Based Modeling and Monitoring Techniques for Future Intelligent Elastic Optical Networks

Decentralizing machine-learning-based QoT estimation for sliceable optical networks

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