Infinera Corporation

Juniper Networks, Inc.

Ankur Neog

Sanjeev Kumar Mishra

Sanjeev Ramachandran

Baranidhar Ramanathan

Zhong Pan

Omer Faruk Yilmaz

Saurabh Kumar

Juraj Slovak

Ravindran Thangarajah

Santosh Kottanipral Mathews

Ramakrishnan Rajagopalan

Shamantha Krishna K G

Disclosed herein are methods and systems configuring a raman amplifier. One exemplary system may be provided with an optical amplifier deployed in an optical network, the optical amplifier having a plurality of raman pumps configured to obtain a desired gain profile. An actual gain profile and a raman pump configuration of each of the plurality of raman pumps may be processed by a network administration device using a first machine learning model to produce a combined optical transmission segment attribute representing attributes of an optical segment of the optical network. The combined optical transmission segment attribute and the desired gain profile may be processed with a second machine learning model to produce corrected raman pump configurations for each of the plurality of raman pumps of the raman amplifier. The corrected raman pump configurations may be used to configure each of the plurality of raman pumps of the raman amplifier.

Correction of raman pump configurations for dynamic compensation of deviations in a raman gain profile

Disclosed herein are methods and systems for computing a launch power for an optical node by collecting data for an optical network segment and inputting the collected data and first power spectral density values into a machine learning model which are used to compute a first non-linear interference value. A first generalized-optical signal-to-noise ratio value is computed using the computed first non-linear interference value and amplified spontaneous emission values. At least one second generalized-optical signal-to-noise ratio value is computed using at least one second non-linear interference value, computed using at least one second power spectral density values, and the amplified spontaneous emission values. A highest generalized-optical signal-to-noise ratio value is determined by comparing the first generalized-optical signal-to-noise ratio value and the at least one second generalized-optical signal-to-noise ratio value. A launch power is computed using the power spectral density values associated with the highest generalized-optical signal-to-noise ratio.

Computation of optimum fiber input power

In general, a device comprising a processor and a memory may be configured to perform various aspects of the techniques described in this disclosure. The processor may conduct, based on configuration parameters, each of a plurality of simulation iterations within the test environment to collect a corresponding plurality of simulation datasets representative of operating states of the network device. The processor may perform a regression analysis with respect to each of the plurality of configuration parameters and each of the plurality of simulation datasets to generate a light weight model representative of the network device that predicts an operating state of the network device. The processor may output the light weight model for use in a computing resource restricted network device to enable prediction of the operating state of the computing resource restricted network device when configured with the configuration parameters. The memory may store the light weight model.

Dynamic prediction of system resource requirement of network software in a live network using data driven models

This disclosure describes techniques that include determining the health of one or more routing engines included within a router. In one example, this disclosure describes a method that includes performing, by a first routing engine included within a router, routing operations, wherein the router includes a plurality of routing engines, including the first routing engine and a second routing engine; receiving, by a computing system, data including health indicators associated with the first routing engine; applying, by the computing system, a machine learning model to the data to determine, from the health indicators, a health status of the first routing engine, wherein the machine learning model has been trained to identify the health status from the health indicators; and determining, by the computing system and based on the health status of the first routing engine, whether to switch routing operations to the second routing engine from the first routing engine.

Routing engine switchover based on health determined by support vector machine

A device may perform a set of sample measurements of a set of slices of spectrum utilized by an input optical signal. The device may perform an analysis of the input optical signal to determine an attenuation profile. The analysis may include identifying a channel in a particular slice of spectrum, of the set of slices of spectrum, based on an optical power of a sample measurement, of the set of sample measurements, satisfying a threshold. The device may apply the attenuation profile to the input optical signal to generate an output optical signal with a particular spectral shape. The device may provide the output optical signal with the particular spectral shape.

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Ankur Neog