Monitoring and data analytics-triggered reconfiguration in partially disaggregated optical networks

©2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.We present ADONIS, which stands for Aggregator/Disaggregator for Optical Network equIpmentS, a novel open device agent able to construct logical network devices from (dis)aggregation of physical components in order to expose meaningful network devices to the SDN controller. We experimentally assess it by means of a control closed-loop involving ADONIS, a Software Defined Network controller, a Monitoring and Data Analytics system, and a novel reconfiguration tool, SMART-A.The research leading to these results has received funding from the EC through the METRO-HAUL (G.A. nº 761727).Peer ReviewedPostprint (author's final draft

Demonstration of monitoring and data analytics-triggered reconfiguration in partially disaggregated optical networks

©2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.We demonstrate a novel agent for optical disaggregated optical networks. When the Monitoring and Data Analytics detects a degradation, it recommends the SDN controller to trigger a network reconfiguration computed by a novel planning tool.The research leading to these results has received funding from the EC through the METRO-HAUL (G.A. nº 761727).Peer ReviewedPostprint (author's final draft

Cognitive and Autonomous Software-Defined Open Optical Networks

L'abstract è presente nell'allegato / the abstract is in the attachmen

Evaluation of the impact of resource disaggregation in future optical transport networks

The communications industry is experiencing constant transformation. Telecom operators consider Network Disaggregation a potential approach to redesign their current network architecture in order to thrive. It focuses on decoupling the optical transport hardware into independent functional blocks, combined with open-source orchestration systems, to abstract the control layer from the physical layer, acquiring software-based control and automation features. The key perceived benefits include reducing vendor lock-in, drive innovation and evolution, and offer flexibility to deploy the best-in-class equipment that fits the network needs, regardless of the supplier. However, there is uncertainty regarding the performance of a disaggregated system, with the introduction of interoperable open nodes to the network, a demerit on the reach of the propagated optical signals is expected compared to a traditional single-vendor system. In this regard, this thesis evaluates the impact of disaggregation on the network performance, specifically, considering several levels of lightpaths' reach reduction, the average number of connections which are denied service is estimated. Since signal regeneration emerges as the straight answer to overcome the reach limitations, its influence on the behavior of the network is analyzed as well. Moreover, the traffic grooming concept is introduced as an alternative to improve the network performance. The idea is to take benefit of the additional optical to electrical conversions performed for signal regeneration and groom low rate traffic streams in the already established lightpaths, looking to increase the capacity of the network. To this end, a network simulator was developed to test a disaggregated system under several conditions as varying the number of regenerators available in the network, allowing or not traffic grooming, and applying different modulation formats or connections' traffic profiles, all of them are compared to the behavior of a traditional integrated system. Overall the simulation results demonstrate an important demerit of the performance of the network as a function of the transmission reach reduction, forcing the use of additional regenerator nodes. Nevertheless, when implementing traffic grooming with a limited number of regenerators, a significant decrease of the network blocking probability is achieved. Thus, network operators must analyze if this benefit plus the advantages of an open model prevails to the cost generated by the additional OEO nodes

Study and application of spectral monitoring techniques for optical network optimization

One of the possible ways to address the constantly increasing amount of heterogeneous and variable internet traffic is the evolution of the current optical networks towards a more flexible, open, and disaggregated paradigm. In such scenarios, the role played by Optical Performance Monitoring (OPM) is fundamental. In fact, OPM allows to balance performance and specification mismatches resulting from the disaggregation adoption and provides the control plane with the necessary feedback to grant the optical networks an adequate automation level. Therefore, new flexible and cost-effective OPM solutions are needed, as well as novel techniques to extract the desired information from the monitored data and process and apply them. In this dissertation, we focus on three aspects related to OPM. We first study a monitoring data plane scheme to acquire the high resolution signal optical spectra in a nonintrusive way. In particular, we propose a coherent detection based Optical Spectrum Analyzer (OSA) enhanced with specific Digital Signal Processing (DSP) to detect spectral slices of the considered optical signals. Then, we identify two main placement strategies for such monitoring solutions, enhancing them using two spectral processing techniques to estimate signal- and optical filter-related parameters. Specifically, we propose a way to estimate the Amplified Spontaneous Emission (ASE) noise or its related Optical Signal-to-Noise (OSNR) using optical spectra acquired at the egress ports of the network nodes and the filter central frequency and 3/6 dB bandwidth, using spectra captured at the ingress ports of the network nodes. To do so, we leverage Machine Learning (ML) algorithms and the function fitting principle, according to the considered scenario. We validate both the monitoring strategies and their related processing techniques through simulations and experiments. The obtained results confirm the validity of the two proposed estimation approaches. In particular, we are able to estimate in-band the OSNR/ASE noise within an egress monitor placement scenario, with a Maximum Absolute Error (MAE) lower than 0.4 dB. Moreover, we are able to estimate the filter central frequency and 3/6 dB bandwidth, within an ingress optical monitor placement scenario, with a MAE lower than 0.5 GHz and 0.98 GHz, respectively. Based on such evaluations, we also compare the two placement scenarios and provide guidelines on their implementation. According to the analysis of specific figures of merit, such as the estimation of the Signal-to-Noise Ratio (SNR) penalty introduced by an optical filter, we identify the ingress monitoring strategy as the most promising. In fact, when compared to scenarios where no monitoring strategy is adopted, the ingress one reduced the SNR penalty estimation by 92%. Finally, we identify a potential application for the monitored information. Specifically, we propose a solution for the optimization of the subchannel spectral spacing in a superchannel. Leveraging convex optimization methods, we implement a closed control loop process for the dynamical reconfiguration of the subchannel central frequencies to optimize specific Quality of Transmission (QoT)-related metrics. Such a solution is based on the information monitored at the superchannel receiver side. In particular, to make all the subchannels feasible, we consider the maximization of the total superchannel capacity and the maximization of the minimum superchannel subchannel SNR value. We validate the proposed approach using simulations, assuming scenarios with different subchannel numbers, signal characteristics, and starting frequency values. The obtained results confirm the effectiveness of our solution. Specifically, compared with the equally spaced subchannel scenario, we are able to improve the total and the minimum subchannel SNR values of a four subchannel superchannel, of 1.45 dB and 1.19 dB, respectively.Una de las posibles formas de hacer frente a la creciente cantidad de tráfico heterogéneo y variable de Internet es la evolución de las actuales redes ópticas hacia un paradigma más flexible, abierto y desagregado. En estos escenarios, el papel que desempeña el modulo óptico de monitorización de prestaciones (OPM) es fundamental. De hecho, el OPM permite equilibrar los desajustes de rendimiento y especificación, los cuales surgen con la adopción de la desagregación; del mismo modo el OPM también proporciona al plano de control la realimentación necesaria para otorgar un nivel de automatización adecuado a las redes ópticas. En esta tesis, nos centramos en tres aspectos relacionados con el OPM. En primer lugar, estudiamos un esquema de monitorización para adquirir, de forma no intrusiva, los espectros ópticos de señales de alta resolución. En concreto, proponemos un analizador de espectro óptico (OSA) basado en detección coherente y mejorado con un específico procesado digital de señal (DSP) para detectar cortes espectrales de las señales ópticas consideradas. A continuación, presentamos dos técnicas de colocación para dichas soluciones de monitorización, mejorándolas mediante dos técnicas de procesamiento espectral para estimar los parámetros relacionados con la señal y el filtro óptico. Específicamente, proponemos un método para estimar el ruido de emisión espontánea amplificada (ASE), o la relación de señal-ruido óptica (OSNR), utilizando espectros ópticos adquiridos en los puertos de salida de los nodos de la red. Del mismo modo, estimamos la frecuencia central del filtro y el ancho de banda de 3/6 dB, utilizando espectros capturados en los puertos de entrada de los nodos de la red. Para ello, aprovechamos los algoritmos de Machine Learning (ML) y el principio de function fitting, según el escenario considerado. Validamos tanto las estrategias de monitorización como las técnicas de procesamiento mediante simulaciones y experimentos. Se puede estimar en banda el ruido ASE/OSNR en un escenario de colocación de monitores de salida, con un Maximum Absolute Error (MAE) inferior a 0.4 dB. Además, se puede estimar la frecuencia central del filtro y el ancho de banda de 3/6 dB, dentro de un escenario de colocación de monitores ópticos de entrada, con un MAE inferior a 0.5 GHz y 0.98 GHz, respectivamente. A partir de estas evaluaciones, también comparamos los dos escenarios de colocación y proporcionamos directrices sobre su aplicación. Según el análisis de específicas figuras de mérito, como la estimación de la penalización de la relación señal-ruido (SNR) introducida por un filtro óptico, demostramos que la estrategia de monitorización de entrada es la más prometedora. De hecho, utilizar un sistema de monitorización de entrada redujo la estimación de la penalización del SNR en un 92%. Por último, identificamos una posible aplicación para la información monitorizada. En concreto, proponemos una solución para la optimización del espaciado espectral de los subcanales en un supercanal. Aprovechando los métodos de optimización convexa, implementamos un proceso cíclico de control cerrado para la reconfiguración dinámica de las frecuencias centrales de los subcanales con el fin de optimizar métricas específicas relacionadas con la calidad de la transmisión (QoT). Esta solución se basa en la información monitorizada en el lado del receptor del supercanal. Validamos el enfoque propuesto mediante simulaciones, asumiendo escenarios con un diferente número de subcanales, distintas características de la señal, y diversos valores de la frecuencia inicial. Los resultados obtenidos confirman la eficacia de nuestra solución. Más específicatamente, en comparación con el escenario de subcanales igualmente espaciados, se pueden mejorar los valores totales y minimos de SNR de los subcanales de un supercanal de cuatro subcanales, de 1.45 dB y 1.19 dB, respectivamentePostprint (published version

An Overview on Application of Machine Learning Techniques in Optical Networks

Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, Machine Learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude the paper proposing new possible research directions

Machine Learning for Multi-Layer Open and Disaggregated Optical Networks

L'abstract è presente nell'allegato / the abstract is in the attachmen

An Overview on Application of Machine Learning Techniques in Optical Networks

Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, machine learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing, and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude this paper proposing new possible research directions

Experimental demonstration of fully disaggregated white box including different types of transponders and monitors, controlled by NETCONF and YANG

We experimentally demonstrated a fully disaggregated white box composed of two different types of transponders, monitors (including filtering effect parameters), add-drop multiplexers, and switches. NETCONF and YANG control the hardware