296 research outputs found

    Fault management based on machine learning

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    © 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.Machine Learning (ML) brings many benefits for network operation. In this paper, basic ML concepts and its integration into existing network control and management planes are reviewed. Case studies covering fault management are illustrated.Peer ReviewedPostprint (author's final draft

    CĂłmo evaluar continua e individualmente en asignaturas basadas en proyectos

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    En este artículo se describe el diseño de la asignatura de Projecte de Xarxes de Computadors i Sistemes Operatius (PXCSO) [1] de la Facultat d'Informàtica de Barcelona (FIB) [2], de la Universitat Politècnica de Catalunya (UPC) [3]. Una asignatura de proyectos, que tiene por objetivo dotar a los alumnos de ingeniería informática de un conjunto de competencias tanto técnicas como no técnicas. La asignatura está diseñada entorno a la ejecución de un proyecto informático, en el que los alumnos trabajan en grupos de 8. Se ha implantado un sistema de evaluación continua e individualizada, que permite que, tanto el profesor como el alumno, tengan una percepción del rendimiento del trabajo individual, proporcionando una realimentación que sirva de estímulo a los estudiantes dentro de cada grupo. Este modelo constituye la experiencia previa más similar al mercado laboral.Peer Reviewe

    Flexible fog computing and telecom architecture for 5G networks

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    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

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

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    © 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/kG/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]

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    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?

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    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

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

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    © 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]

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    © [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

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

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    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
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