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%

On Cooperative Fault Management in Multi-Domain Optical Networks Using Hybrid Learning

This paper presents a hybrid learning approach for cooperative fault management in multi-domain optical networks (MD-ONs). The proposed approach relies on a broker-based MD-ON architecture for coordination of inter-domain service provisioning. We first propose a self-supervised learning design for soft failure detection. The self-supervised learning design makes use of a clustering algorithm for extracting normal and abnormal patterns from optical performance monitoring data and a supervised learning-based classifier trained with the learned patterns for online detection. To facilitate high soft failure detection accuracy in the absence of sufficient abnormal data for training, the proposed design estimates model uncertainties during predictions and identifies instances associated with high uncertainties as also soft failures. Then, we extend the self-supervised learning design and present a federated learning framework for the broker plane and DMs to learn cooperatively while complying with the privacy constraints of each domain. Finally, a data-driven soft failure localization scheme that operates by analyzing the patterns of data is proposed as a complement to the existing approaches. Performance evaluations indicate that the self-supervised learning design can achieve soft failure detection accuracy of up to ∼ 97% with 0.01%-0.04% false alarm rate, while federated learning enables DMs to realize >90% soft failure detection rates in the cases of highly unbalanced data distribution (two of the three domains possess zero abnormal data for training)

The Jamming Perspective on Wet Foams

Amorphous materials as diverse as foams, emulsions, colloidal suspensions and granular media can {\em jam} into a rigid, disordered state where they withstand finite shear stresses before yielding. The jamming transition has been studied extensively, in particular in computer simulations of frictionless, soft, purely repulsive spheres. Foams and emulsions are the closest realizations of this model, and in foams, the (un)jamming point corresponds to the wet limit, where the bubbles become spherical and just form contacts. Here we sketch the relevance of the jamming perspective for the geometry and flow of foams --- and also discuss the impact that foams studies may have on theoretical studies on jamming. We first briefly review insights into the crucial role of disorder in these systems, culminating in the breakdown of the affine assumption that underlies the rich mechanics near jamming. Second, we discuss how crucial theoretical predictions, such as the square root scaling of contact number with packing fraction, and the nontrivial role of disorder and fluctuations for flow have been observed in experiments on 2D foams. Third, we discuss a scaling model for the rheology of disordered media that appears to capture the key features of the flow of foams, emulsions and soft colloidal suspensions. Finally, we discuss how best to confront predictions of this model with experimental data.Comment: 7 Figs., 21 pages, Review articl

Root Cause Analysis for Autonomous Optical Network Security Management

The ongoing evolution of optical networks towards autonomous systems supporting high-performance services beyond 5G requires advanced functionalities for automated security management. To cope with evolving threat landscape, security diagnostic approaches should be able to detect and identify the nature not only of existing attack techniques, but also those hitherto unknown or insufficiently represented. Machine Learning (ML)-based algorithms perform well when identifying known attack types, but cannot guarantee precise identification of unknown attacks. This makes Root Cause Analysis (RCA) crucial for enabling timely attack response when human intervention is unavoidable. We address these challenges by establishing an ML-based framework for security assessment and analyzing RCA alternatives for physical-layer attacks. We first scrutinize different Network Management System (NMS) architectures and the corresponding security assessment capabilities. We then investigate the applicability of supervised and unsupervised learning (SL and UL) approaches for RCA and propose a novel UL-based RCA algorithm called Distance-Based Root Cause Analysis (DB-RCA). The framework’s applicability and performance for autonomous optical network security management is validated on an experimental physical-layer security dataset, assessing the benefits and drawbacks of the SL-and UL-based RCA. Besides confirming that SL-based approaches can provide precise RCA output for known attack types upon training, we show that the proposed UL-based RCA approach offers meaningful insight into the anomalies caused by novel attack types, thus supporting the human security officers in advancing the physical-layer security diagnostics

Shear Deformation in Polymer Gels and Dense Colloidal Suspensions

This thesis investigates two soft-matter systems, viz., bio-polymer gels and colloidal dispersions under mechanical deformation, to study non-affinity and jamming. Most materials are assumed to deform affinely, i.e., macroscopic applied deformations are assumed to translate uniformly to the microscopic level. This thesis explores the validity of the affine assumption in model polymer networks under shear. Displacements of micron-sized fluorescent polystyrene tracer beads embedded in polyacrylamide (PA) gels are quantified when the sample is sheared. The experiments confirm that the macroscopic elasticity of PA gels behaves in accordance with traditional flexible polymer network elasticity theory. Microscopically, non-affine deformation is detected, and the observations are in qualitative agreement with many aspects of current theories of polymer network non-affinity. The measured non-affinity in PA gels suggests the presence of structural inhomogeneities resulting from the reaction kinetics, which likely predominates over the effects of thermal fluctuations. Compared to flexible polymer gels, filamentous biopolymer networks generally have higher shear moduli, exhibit a striking increase in elastic modulus with increasing strain, and show pronounced negative normal stress when deformed under shear. Affine deformation is an essential assumption in the theories of these materials. The validity of this assumption is experimentally tested in fibrin and collagen gels. Measurements demonstrate that non-affine deformation is small for networks of thinner, relatively flexible filaments and decreases even further as strain increases into the non-linear regime. Many observations are consistent with the entropic nonlinear elasticity model for semiflexible polymer networks. However, when filament stiffness and mesh-size increase, then deformations become more non-affine and the observations appear to be consistent with enthalpic bending and stretching models. A qualitatively different set of studies explores the rheology of monodisperse and bidisperse colloidal suspensions near the jamming transition as a function of packing fraction, steady-state strain rate, and oscillatory shear frequency. The experiments employ soft, temperature-sensitive polymer micro-spheres for easy tuning of sample packing fraction and a rheometer in order to explore scaling behaviors of shear stress versus strain rate, and storage/loss shear moduli versus frequency. Under steady shear, rheometer measurements exhibit predicted scaling behavior for volume fractions above and below the jamming transition that agree with scaling observed in monodisperse particle suspensions by microfluidic rheology; importantly, similar scaling behavior is observed for the first time in bidisperse particle suspensions. At finite frequency, new measurements were performed across the jamming transition for both monodisperse and bidisperse suspensions. The storage and loss moduli of the jammed systems, measured as a function of frequency and volume fraction, could be scaled onto two distinct master curves in agreement with simulation predictions [142]. For unjammed systems, stress-relaxation timescale is found to scale with volume fraction

Self-Taught Anomaly Detection With Hybrid Unsupervised/Supervised Machine Learning in Optical Networks

This paper proposes a self-taught anomaly detection framework for optical networks. The proposed framework makes use of a hybrid unsupervised and supervised machine learning scheme. First, it employs an unsupervised data clustering module (DCM) to analyze the patterns of monitoring data. The DCM enables a self-learning capability that eliminates the requirement of prior knowledge of abnormal network behaviors and therefore can potentially detect unforeseen anomalies. Second, we introduce a self-taught mechanism that transfers the patterns learned by the DCM to a supervised data regression and classification module (DRCM). The DRCM, whose complexity is mainly related to the scale of the applied supervised learning model, can potentially facilitate more scalable and time-efficient online anomaly detection by avoiding excessively traversing the original dataset. We designed the DCM and DRCM based on the density-based clustering algorithm and the deep neural network structure, respectively. Evaluations with experimental data from two use cases (i.e., single-point detection and end-to-end detection) demonstrate that up to 99% anomaly detection accuracy can be achieved with a false positive rate below 1%