Flexible fog computing and telecom architecture for 5G networks

We review a novel, secure, highly distributed and ultra-dense fog computing infrastructure, which can be allocated at the extreme edge of a wired/wireless network for a Telecom Operator to provide multiple unified, cost-effective and new 5G services, such as Network Function Virtualization (NFV), Mobile Edge Computing (MEC), and services for third parties (e.g., smart cities, vertical industries or Internet of Things (IoT)). The distributed and programmable fog technologies are expected to strengthen the position of the Mobile Network and cloud markets; key benefits are the dynamic deployment of new distributed low-latency services. The architecture consists of three main building blocks: a) a scalable node, that is seamlessly integrated in the Telecom infrastructure; b) a controller, focused on service assurance, that is integrated in the management and orchestration architecture of the Telecom operator; and c) services running on top of the Telecom infrastructure.Peer ReviewedPostprint (author's final draft

Optical network automation [Invited tutorial]

© 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.Increased levels of automation will be necessary in view of more stringent performance requirements that next generation optical transport networks need to support in a near term, not only for high capacity, but, even more importantly, for dynamicity, latency, and availability. All these aspects will become more relevant with the growing complexity of modern networks. Network automation targets resource reoptimization to rapidly adapt the network to the expected conditions, quick degradation detection to improve the quality of the connections, as well as failure detection and identification to facilitate maintenance. Network automation requires and implies the collection of data for performance monitoring, being then elaborated by data analytics algorithms to produce meaningful inputs for the network controller, which will finally program the underlying devices. In this paper, we analyze alternative architectures for monitoring and data analytics (MDA) and illustrative control loops are presented aiming at validating the usefulness of MDA to automate optical networks operation.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 (author's final draft

Supporting time-sensitive and best-effort traffic on a common metro infrastructure

© 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.Considerable research and standardization efforts are being made to support time-sensitive traffic, e.g., generated by applications like Industry 4.0 and 5G fronthaul, on packet networks. This letter focuses on analyzing the impact of conveying time-sensitive traffic in operators’ networks when such traffic is mixed with best-effort traffic. Extensions to a continuous $G/G/1/k$ queue model are proposed to evaluate two different Ethernet technologies, synchronous and asynchronous, supporting time-sensitive flows in terms of their influence on the performance of best-effort traffic. Results highlight pros and cons of those technologies to protect best-effort performance.The research leading to these results has received funding from the European Commission for the H2020-ICT-2016- 2 METRO-HAUL project (G.A. 761727), from the AEI/FEDER TWINS project (TEC2017-90097-R), and from the Catalan Institution for Research and Advanced Studies (ICREA).Peer ReviewedPostprint (author's final draft

Applications of digital twin for autonomous zero-touch optical networking [Invited]

Huge efforts have been paid lastly to study the application of Machine Learning techniques to optical transport networks. Applications include Quality of Transmission (QoT) estimation, failure and anomaly detection, and network automation, just to mention a few. In this regard, the development of Optical Layer Digital Twins able to accurately model the optical layer, reproduce scenarios, and generate expected signals are of paramount importance. In this paper, we introduce two applications of Optical Layer Digital Twins namely, misconfiguration detection and QoT estimation. Illustrative results show the accuracy and usefulness of the proposed applications.The research leading to these results has received funding from the European Community through the MSCA MENTOR (G.A. 956713) and the HORIZON SEASON (G.A. 101096120) projects, the AEI through the IBON (PID2020-114135RB-I00) project, and by the ICREA institution.Peer ReviewedPostprint (author's final draft

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

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

Knowledge management in optical networks: architecture, methods, and use cases [Invited]

© [2019 Optical Society of America]. Users may use, reuse, and build upon the article, or use the article for text or data mining, so long as such uses are for non-commercial purposes and appropriate attribution is maintained. All other rights are reserved.Autonomous network operation realized by means of control loops, where prediction from machine learning (ML) models is used as input to proactively reconfigure individual optical devices or the whole optical network, has been recently proposed to minimize human intervention. A general issue in this approach is the limited accuracy of ML models due to the lack of real data for training the models. Although the training dataset can be complemented with data from lab experiments and simulation, it is probable that once in operation, events not considered during the training phase appear and thus lead to model inaccuracies. A feasible solution is to implement self-learning approaches, where model inaccuracies are used to re-train the models in the field and to spread such data for training models being used for devices of the same type in other nodes in the network. In this paper, we develop the concept of collective self-learning aiming at improving the model’s error convergence time as well as at minimizing the amount of data being shared and stored. To this end, we propose a knowledge management (KM) process and an architecture to support it. Besides knowledge usage, the KM process entails knowledge discovery, knowledge sharing, and knowledge assimilation. Specifically, knowledge sharing and assimilation are based on distributing and combining ML models, so specific methods are proposed for combining models. Two use cases are used to evaluate the proposed KM architecture and methods. Exhaustive simulation results show that model-based KM provides the best error convergence time with reduced data being shared.Peer ReviewedPostprint (author's final draft

Distributed and centralized options for self-learning

© 2019 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 general, the availability of enough real data from real fog computing scenarios to produce accurate Machine Learning (ML) models is rarely ensured since new equipment, techniques, etc., are continuously being deployed in the field. Although an option is to generate data from simulation and lab experiments, such data could not cover the whole features space, which would translate into ML models inaccuracies. In this paper, we propose a self-learning approach to facilitate ML deployment in real scenarios. A dataset for ML training can be initially populated based on the results from simulation and lab experiments and once ML models are generated, ML re-training can be performed after inaccuracies are detected to improve their precision. Illustrative numerical results show the benefits from the proposed self-learning approach for two general use cases of regression and classification.This work was partially supported by the EC through the METRO-HAUL project (G.A. nº 761727), from the AEI/FEDER TWINS project (TEC2017-90097-R), and from the Catalan 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