Autonomous and reliable operation of multilayer optical networks

This Ph.D. thesis focuses on the reliable autonomous operation of multilayer optical networks. The first objective focuses on the reliability of the optical network and proposes methods for health analysis related to Quality of Transmission (QoT) degradation. Such degradation is produced by soft-failures in optical devices and fibers in core and metro segments of the operators’ transport networks. Here, we compare estimated and measured QoT in the optical transponder by using a QoT tool based on GNPy. We show that the changes in the values of input parameters of the QoT model representing optical devices can explain the deviations and degradation in performance of such devices. We use reverse engineering to estimate the value of those parameters that explain the observed QoT. We show by simulation a large anticipation in soft-failure detection, localization and identification of degradation before affecting the network. Finally, for validating our approach, we experimentally observe the high accuracy in the estimation of the modeling parameters. The second objective focuses on multilayer optical networks, where lightpaths are used to connect packet nodes thus creating virtual links (vLink). Specifically, we study how lightpaths can be managed to provide enough capacity to the packet layer without detrimental effects in their Quality of Service (QoS), like added delays or packet losses, and at the same time minimize energy consumption. Such management must be as autonomous as possible to minimize human intervention. We study the autonomous operation of optical connections based on digital subcarrier multiplexing (DSCM). We propose several solutions for the autonomous operation of DSCM systems. In particular, the combination of two modules running in the optical node and in the optical transponder activate and deactivate subcarriers to adapt the capacity of the optical connection to the upper layer packet traffic. The module running in the optical node is part of our Intent-based Networking (IBN) solution and implements prediction to anticipate traffic changes. Our comprehensive study demonstrates the feasibility of DSCM autonomous operation and shows large cost savings in terms of energy consumption. In addition, our study provides a guideline to help vendors and operators to adopt the proposed solutions. The final objective targets at automating packet layer connections (PkC). Automating the capacity required by PkCs can bring further cost reduction to network operators, as it can limit the resources used at the optical layer. However, such automation requires careful design to avoid any QoS degradation, which would impact Service Level Agreement (SLA) in the case that the packet flow is related to some customer connection. We study autonomous packet flow capacity management. We apply RL techniques and propose a management lifecycle consisting of three different phases: 1) a self-tuned threshold-based approach for setting up the connection until enough data is collected, which enables understanding the traffic characteristics; 2) RL operation based on models pre-trained with generic traffic profiles; and 3) RL operation based on models trained with the observed traffic. We show that RL algorithms provide poor performance until they learn optimal policies, as well as when the traffic characteristics change over time. The proposed lifecycle provides remarkable performance from the starting of the connection and it shows the robustness while facing changes in traffic. The contribution is twofold: 1) and on the one hand, we propose a solution based on RL, which shows superior performance with respect to the solution based on prediction; and 2) because vLinks support packet connections, coordination between the intents of both layers is proposed. In this case, the actions taken by the individual PkCs are used by the vLink intent. The results show noticeable performance compared to independent vLink operation.Esta tesis doctoral se centra en la operación autónoma y confiable de redes ópticas multicapa. El primer objetivo se centra en la fiabilidad de la red óptica y propone métodos para el análisis del estado relacionados con la degradación de la calidad de la transmisión (QoT). Dicha degradación se produce por fallos en dispositivos ópticos y fibras en las redes de transporte de los operadores que no causan el corte de la señal. Comparamos el QoT estimado y medido en el transpondedor óptico mediante el uso de una herramienta de QoT basada en GNPy. Mostramos que los cambios en los valores de los parámetros de entrada del modelo QoT que representan los dispositivos ópticos pueden explicar las desviaciones y la degradación en el rendimiento de dichos dispositivos. Usamos ingeniería inversa para estimar el valor de aquellos parámetros que explican el QoT observado. Mostramos, mediante simulación, una gran anticipación en la detección, localización e identificación de fallas leves antes de afectar la red. Finalmente, validamos nuestro método de forma experimental y comprobamos la alta precisión en la estimación de los parámetros de los modelos. El segundo objetivo se centra en las redes ópticas multicapa, donde se utilizan conexiones ópticas (lightpaths) para conectar nodos de paquetes creando así enlaces virtuales (vLink). Específicamente, estudiamos cómo se pueden gestionar los lightpaths para proporcionar suficiente capacidad a la capa de paquetes sin efectos perjudiciales en su calidad de servicio (QoS), como retrasos adicionales o pérdidas de paquetes, y al mismo tiempo minimizar el consumo de energía. Estudiamos el funcionamiento autónomo de conexiones ópticas basadas en multiplexación de subportadoras digitales (DSCM) y proponemos soluciones para su funcionamiento autónomo. En particular, la combinación de dos módulos que se ejecutan en el nodo óptico y en el transpondedor óptico activan y desactivan subportadoras para adaptar la capacidad de la conexión óptica al tráfico de paquetes. El módulo que se ejecuta en el nodo óptico implementa la predicción para anticipar los cambios de tráfico. Nuestro estudio demuestra la viabilidad de la operación autónoma de DSCM y muestra un gran ahorro de consumo de energía. El objetivo final es la automatización de conexiones de capa de paquete (PkC). La automatización de la capacidad requerida por las PkC puede generar una mayor reducción de costes, ya que puede limitar los recursos utilizados en la capa óptica. Sin embargo, dicha automatización requiere un diseño cuidadoso para evitar cualquier degradación de QoS, lo que afectaría acuerdos de nivel de servicio (SLA) en el caso de que el flujo de paquetes esté relacionado con alguna conexión del cliente. Estudiamos la gestión autónoma de la capacidad del flujo de paquetes. Aplicamos RL y proponemos un ciclo de vida de gestión con tres fases: 1) un enfoque basado en umbrales auto ajustados para configurar la conexión hasta que se recopilen suficientes datos, lo que permite comprender las características del tráfico; 2) operación RL basada en modelos pre-entrenados con perfiles de tráfico genéricos; y 3) operación de RL en base a modelos entrenados con el tránsito observado. Mostramos que los algoritmos de RL ofrecen un desempeño deficiente hasta que aprenden las políticas óptimas, así cuando las características del tráfico cambian con el tiempo. El ciclo de vida propuesto proporciona un rendimiento notable desde el inicio de la conexión y muestra la robustez frente a cambios en el tráfico. La contribución es doble: 1) proponemos una solución basada en RL que muestra un rendimiento superior que la solución basada en predicción; y 2) debido a que los vLinks admiten conexiones de paquetes, se propone la coordinación entre las intenciones de ambas capas. En este caso, la intención de vLink utiliza las acciones realizadas por los PkC individuales. Los resultados muestran un rendimiento notable en comparación con la operación independiente de vLink.Postprint (published version

Reliable and accurate autonomous flow operation based on off-line trained reinforcement learning

A RL agent trained offline for reliability and able to refine its policies during online operation is proposed. Results for three illustrative flow automation use cases show remarkable performance with extraordinary adaptability to changes.The research leading to these results has received funding from the Spanish MINECO TWINS project (TEC2017-90097-R) and by the ICREA institution.Peer ReviewedPostprint (published version

Soft-failure localization and time-dependent degradation detection for network diagnosis

© 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.In optical networks, degradation of the Quality of Transmission (QoT) can be the outcome of soft-failures in optical devices, like Optical Transponders, Wavelength Selective Switches (WSS) and Optical Amplifiers (OA). In this paper, we assume time-dependent degradations on ROADMs and OAs. Specifically, several degradations are considered: i) the noise figure can increase linearly over time due to the aging of the components; ii) the maximum of optical output power of the amplifiers can decrease because of the degradation in the pump lasers of the EDFAs; iii) aging effects, e.g., due to fiber splices; and iv) the OSNR can vary caused by frequency drift of WSSs due to temperature variations. Our proposal for degradation detection and soft-failure localization includes algorithms that are able to detect and localize the degradation in early stages and facilitate network diagnosis.In addition, we propose an architecture where the control plane consist of a network controller, a Monitoring and Data Analytics system and a QoT tool based on GNPy that are interconnected with each other.The research leading to these results has received funding from the Spanish MINECO TWINS project (TEC2017-90097-R), and from the Catalan Institution for Research and Advanced Studies (ICREA).Peer ReviewedPostprint (published version

Is intelligence the answer to deal with the 5 V’s of telemetry data?

Telemetry data and big data share volume, velocity, variety, veracity and value characteristics. We propose a distributed telemetry architecture and show how intelligence can help dealing with the 5 V’s of optical networks telemetry data.The research leading to these results has received funding from the HORIZON SEASON (G.A. 101096120) and the MICINN IBON (PID2020-114135RB-I00) projects and from the ICREA Institution.Peer ReviewedPostprint (author's final draft

Distributed and autonomous flow routing based on deep reinforcement learning

A DRL approach with a specific reward function is proposed for autonomous flow routing operating on multilayer scenarios. Illustrative results reveal that the DRL achieves optimal flow routing in terms of cost and service quality. © 2022 IEICE.The research leading to these results has received funding from the H2020 B5G-OPEN (G.A. 101016663), the MINECO-NextGenerationEU TIMING (TSI-063000-2021-145), the MICINN IBON (PID2020-114135RB-I00), and the ICREA Institution.Peer ReviewedPostprint (published version

Packet flow capacity autonomous operation based on reinforcement learning

As the dynamicity of the traffic increases, the need for self-network operation becomes more evident. One of the solutions that might bring cost savings to network operators is the dynamic capacity management of large packet flows, especially in the context of packet over optical networks. Machine Learning, particularly Reinforcement Learning, seems to be an enabler for autonomicity as a result of its inherent capacity to learn from experience. However, precisely because of that, RL methods might not be able to provide the required performance (e.g., delay, packet loss, and capacity overprovisioning) when managing the capacity of packet flows, until they learn the optimal policy. In view of that, we propose a management lifecycle with three phases: (i) a self-tuned threshold-based approach operating just after the packet flow is set up and until enough data on the traffic characteristics are available; (ii) an RL operation based on models pre-trained with a generic traffic profile; and (iii) an RL operation with models trained for real traffic. Exhaustive simulation results confirm the poor performance of RL algorithms until the optimal policy is learnt and when traffic characteristics change over time, which prevents deploying such methods in operators’ networks. In contrast, the proposed lifecycle outperforms benchmarking approaches, achieving noticeable performance from the beginning of operation while showing robustness against traffic changes.This research received funding from the European Community through the B5G-OPEN project (101016663), from the AEI IBON project (PID2020-114135RB-I00), and from the ICREA Institution.Peer ReviewedPostprint (published version

Intent-based networking and its application to optical networks [invited tutorial]

The intent-based networking (IBN) paradigm targets defining high-level abstractions so network operators can define what their desired outcomes are without specifying how they would be achieved. The latter can be achieved by leveraging network programmability, monitoring, and data analytics, as well as the key assurance component. In this tutorial, we introduce the IBN paradigm and its application to optical networking, highlighting the benefits that machine learning (ML) algorithms can provide to IBN. Because the deployment of ML applications requires a specific orchestrator to create ML functions that are connected as ML pipelines, we show an implementation of such an orchestrator. Some challenges and solutions are presented for the generation of accurate synthetic data, proactive self-configuration, and cooperative intent operation. Illustrative examples of intent-based operation and numerical results are presented, and the obtained performance is discussed.The research leading to these results has received funding from the MICINN IBON (PID2020-114135RB-I00) project and from the ICREA Institution.Peer ReviewedPostprint (author's final draft

Combining long-short term memory and reinforcement learning for improved autonomous network operation

A combined LSTM and RL approach is proposed for dynamic connection capacity allocation. The LSTM predictor anticipates periodical long-term sharp traffic changes and extends short-term RL knowledge. Numerical results show remarkable performance.The research leading to these results has received funding from the Spanish MINECO TWINS project (TEC2017-90097-R) and by the ICREA institution.Peer ReviewedPostprint (published version

Degradation detection and severity estimation by exploiting an optical time and frequency digital twin

We exploit the intrinsic advantages of a time and frequency domain digital twin to detect degradations and to estimate their severity. Noticeable performance shown for filter failures confirms the usefulness of this approach.The research leading to these results has received funding from the European Community through the MSCA MENTOR (G.A. 956713) and the H2020 B5G-OPEN (G.A. 101016663) projects, the AEI through the IBON (PID2020-114135RB-I00) project, and by the ICREA institutionPeer ReviewedPostprint (author's final draft

Coordination of radio access and optical transport

New 5G and beyond applications demand strict delay requirements. In this paper, we propose coordination between radio access and optical transport to guarantee such delay while optimizing optical capacity allocation. Illustrative results show near real-time autonomous capacity adaptation benefits based on radio access delay requirements.The research leading to these results has received funding from the HORIZON SEASON (G.A. 101096120), the UNICO5G TIMING (TSI-063000-2021-145), and the MICINN IBON (PID2020-114135RB-I00) projects and from the ICREA institution.Peer ReviewedPostprint (author's final draft