Open optical network planning demonstration

Augé, Jean-Luc; Grammel, Gert; Rouzic, Esther Le; Curri, Vittorio; Galimberti, Gabriele; Powell, J. Myles

doi:10.1364/ofc.2019.m3z.9

Cited by 12 publications

(6 citation statements)

References 4 publications

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“…ODTN has also been extended to be capable of leveraging the GNPy optical simulation and planning tool [22] for path computation across the optical domain. Through the use of GNPy ODTN is now aware of different optical impairments, such as fiber loss or device gain capabilities.…”

Section: Recent Odtn Activitiesmentioning

confidence: 99%

Reliable Optical Networks With ODTN: Resiliency and Fail-Over in Data and Control Planes

Campanella

Yan

Casellas

et al. 2020

J. Lightwave Technol.

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This paper reports a technical demonstration showing the use of the ONOS SDN controller for disaggregated transport networks, summarizing the latest developments and results within the Open Networking Foundation (ONF) Open Disaggregated Transport Networks (ODTN) project. The demonstration mainly covers the dynamic provisioning of data connectivity services and advanced automatic failure recovery, both at the control and data plane levels. For the provisioning, we demonstrate the usage of open and standard protocols and interfaces. For the recovery part, we first demonstrate, covering the control plane, how ONOS can behave as a logically centralized controller while using multiple coordinated instances for robustness. We show how the different devices remain under control even in the event of a failure of one of such instances, relying on the ATOMIX framework and the dynamic real-time negotiation of device mastership. For the data plane, we demonstrate the capabilities of the controller to perform automatic restoration of optical services.

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Section: Recent Odtn Activitiesmentioning

confidence: 99%

Reliable Optical Networks With ODTN: Resiliency and Fail-Over in Data and Control Planes

Campanella

Yan

Casellas

et al. 2020

J. Lightwave Technol.

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“…In these regimes, a software-defined networking (SDN) approach to optical network control and management may be implemented, with lightpaths (LP)s being assigned dynamically. This allows the wavelength division multiplexed (WDM) optical transport to be implemented in a fully virtual fashion with a common application programming interface (API), in order to independently manage network subsystems and components [7]. To achieve this, a quality of transmission estimator (QoT-E) must be used to compute the generalized signal-to-noise ratio (GSNR) of transparent LPs to assess network performance before, after and during deployment, enabling implementation of the lightpath computation engine (L-PCE).…”

Section: Introductionmentioning

confidence: 99%

Enhancing Lightpath QoT Computation With Machine Learning in Partially Disaggregated Optical Networks

D’Amico

Straullu

Borraccini

et al. 2021

IEEE Open J. Commun. Soc.

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Increasing traffic demands are causing network operators to adopt disaggregated and open networking solutions to better exploit optical transmission capacity, and consequently enable a softwaredefined networking (SDN) approach to control and management that encompasses the WDM data transport layer. In these frameworks, a quality of transmission estimator (QoT-E) that gives the generalized signalto-noise ratio (GSNR) is commonly used to compute the feasibility of transparent lightpaths (LP)s, taking into account the amplified spontaneous emission (ASE) noise and the nonlinear interference (NLI). In general, the ASE noise is the main contributor to the GSNR and is also the most challenging noise component to evaluate in a scenario with varying spectral loads, due to fluctuations in the optical amplifier responses. In this work, we propose a machine learning (ML) algorithm that is trained using different ASE-shaped spectral loads in order to predict the OSNR component of the GSNR; this methodology is subsequently used in combination with a QoT-E in the lightpath computation engine (L-PCE). We present an experiment on a point-to-point optical line system (OLS), including 9 commercial erbium-doped fiber amplifiers (EDFA)s used as black-boxes, each with variable gain and tilt values, and 8 fibers that are characterized by distinct physical parameters. Within this experiment, we receive the signal at the end of the OLS, measuring the bit-error-rate (BER) and the power spectrum, over 2520 different spectral loads. From this dataset, we extract the expected GSNRs and their linear and nonlinear components. Through joint application of a ML algorithm and the open-source GNPy library, we obtain a complete QoT-E, demonstrating that a reliable and accurate LP feasibility predictor may be implemented.INDEX TERMS Machine learning, optical communications, erbium doped fiber amplifier (EDFA), Raman scattering, quality of transmission (QoT).

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“…Correspondingly, optical networks are shifting to a format that enables a greater degree of flexibility with respect to wavelength assignment, transmission standards and symbol rates, representing a shift towards a disaggregated structure [4]- [6] . A fully disaggregated network approach requires all OLS components to be considered independently [7] , as they may be controlled by different network providers and operators. In this scenario the lightpath (LP) history may not be known, presenting a challenge when attempting to model the physical layer: the nonlinear interference (NLI) produced during propagation is coherent [8], [9] , meaning that the NLI accumulation depends upon the configurations of all previously crossed fiber spans.…”

Section: Introductionmentioning

confidence: 99%

Simulative assessment of non-linear interference generation within disaggregated optical line systems

London¹,

Virgillito²,

D’Amico³

et al. 2020

OSA Continuum

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Open optical network planning demonstration

Cited by 12 publications

References 4 publications

Reliable Optical Networks With ODTN: Resiliency and Fail-Over in Data and Control Planes

Reliable Optical Networks With ODTN: Resiliency and Fail-Over in Data and Control Planes

Enhancing Lightpath QoT Computation With Machine Learning in Partially Disaggregated Optical Networks

Simulative assessment of non-linear interference generation within disaggregated optical line systems

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