Ciena Corporation

Cienna Corporation

David Côté

Thomas Triplet

Emil Janulewicz

Petar Djukic

Olivier Simard

Todd D Morris

Yinqing Pei

Shervin Shirmohammadi

Shelley A Bhalla

Merlin Davies

Lyndon Y Ong

Aung Htay

Cengiz Alaettinoglu

Romualdas Armolavicius

Raghuraman Ranganathan

Roger Michael Elbaz

Christopher Janz

Arslan Shahid

Gauravdeep Singh Shami

Sa'di Altamimi

Christopher Barber

Yan Liu

Shady A Mohammed

Mitchell Howard Auster

David W Boertjes

Yang Yu

Qunbi Zhuge

Babak Esfandiari

Sergio Slobodrian

Christian Desrosiers

William Webb

Bruck Wubete

William Kaufmann

Logan Blyth

Saurabh Dinesh Brahmankar

Sudhan Puranik

Mark Gibson

Christine Tremblay

Magdi Wissa

Philippe Alain Ngani Sigue

Ali Mahmoudialami

Basel Altamimi

Phillip Doelling

Paul Gosse

Yogeshwar Chatur Deore

Kevin Kim

Dana Dennis

Thomas Kunz

Minming Ni

Patrick Premont

Ayse Rumeysa Mohammed

Maheshwaran Venkatachalam

Shelly A Bhalla

Methods are provided for recommending actions to improve operability of a network. In one implementation, a method includes acknowledging a plurality of subnetworks in a whole network, each subnetwork including multiple nodes and being represented by a tunnel group having multiple end-to-end tunnels through the subnetwork. The method also includes selecting a first group of subnetworks from the plurality of subnetworks and generating a Reinforcement Learning (RL) agent for each subnetwork of the first group. Each RL agent is based on observations of end-to-end metrics of the end-to-end tunnels of the respective subnetwork. The observations are independent of specific topology information of the subnetwork. Also, the method includes training a global model based on the RL agents of the first group of subnetworks and applying the global model to an Action Recommendation Engine (ARE) configured for recommending actions that can be taken to improve a state of the whole network.

Creating a global reinforcement learning (RL) model from subnetwork RL agents

Systems, methods, and computer-readable media are provided for logging long-term data and analyzing the long-term data with short-term data to determine the health of fiber connections in an optical network. A method, according to one implementation, includes a step of obtaining data associated with performance of fiber connections of an optical network. The fiber connections include at least an inter-node fiber connecting two adjacent network nodes and an intra-node fiber connection connecting two photonic devices within each of the two adjacent network nodes. The method further includes the step of logging the data over time as historical data and then analyzing the health of the fiber connections based on the historical data and newly-obtained data. Also, the method includes displaying a report on an interactive user interface, whereby the report is configured to show the health of the fiber connections.

Analyzing performance of fibers and fiber connections using long-term historical data

Systems and methods are provided for predicting an impending change to Interior Gateway Protocol (IGP) metrics associated with Network Elements (NEs) in an optical network. A method, according to one implementation, includes receiving Performance Monitoring (PM) data related to an optical network having a plurality of links. The method also includes analyzing the PM data to predict the likelihood of an impending change to an IGP metric associated with a problematic link of the plurality of links.

Predicting impending change to interior gateway protocol (IGP) metrics

Systems and methods include obtaining network data including first data of devices and services in the network, Performance Monitoring (PM) data associated with the devices and services and with associated timestamps, and second data including any of tickets, alarms, and events affecting some of the devices and services and with associated timestamps; obtaining one or more target events from the second data based on associated operational impact in the network; determining the PM data that is statistically correlated with the one or more target events; determining the statistically correlated PM data over a corresponding time based on the associated timestamps of the PM data and the one or more target events; and providing labels for the determined statistically correlated PM data with an associated label based on the associated target event of the one or more target events.

Automatic labeling of telecommunication network data to train supervised machine learning

Systems and methods include steps of determining a state of a network based on telemetry data; determining a value of a reward associated with the state; determining an action to take on the network to bring the network to a next state that is expected to have a better than or equal to value of the reward; and causing the action to be implemented in the network. The steps can also include continuing the determining steps and the causing step.

Reinforcement learning for autonomous telecommunications networks

Artificial Intelligence (AI)-based network control includes obtaining data from a network having a plurality of network elements; analyzing the data with one or more Machine Learning (ML) algorithms to determine one or more actions for network control; analyzing the determined one or more actions to determine any risks associated therewith; and one of allowing, modifying, and blocking the determined one or more actions based on the determined risks to safeguard the network. The risks can be based on one or more of (1) non-deterministic behavior AI inference which is statistical in nature, (2) unbounded uncertainty of the AI inference that can result in arbitrarily large inaccuracy on rare occasions, (3) unpredictable behavior of the AI inference in presence of input data that is different than data in training and testing datasets, and (4) malicious input data.

Safeguarding artificial intelligence-based network control

Systems and methods are provided for analyzing one or more root causes of service degradation events in a network or other environment. A method, according to one implementation, includes a step of monitoring a plurality of overlying services offered in an underlying infrastructure having a plurality of resources arranged with a specific topology. In response to detecting a negative impact on the overlying services during a predetermined time window and based on an understanding of the specific topology, the method further includes the step of identifying suspect components from the plurality of resources in the underlying infrastructure. The method also includes the step of obtaining status information with respect to the suspect components to determine a root cause of the negative impact on the overlying services.

Identifying root causes of network service degradation

Systems and methods include receiving a machine learning model that is configured to detect anomalies in network devices operating in a multi-layer network, wherein the machine learning model is trained via unsupervised learning that includes training the machine learning model with unlabeled data that describes an operational status of the network devices over time; receiving live data related to a current operational status of the network devices; analyzing the live data with the machine learning model; and detecting an anomaly related to any of the network device based on the analyzing.

Systems and methods to detect abnormal behavior in networks

Systems, methods, and computer-readable media including software logic are provided for optimizing Border Gateway Protocol (BGP) traffic in a telecommunications network. In one embodiment, systems and methods include, with a current state of one or more inter-Autonomous Systems (AS) links, causing performance of an action in the telecommunication network, determining a metric based on the action to determine an updated current state of the one or more inter-AS links, and utilizing the metric to perform a further action to achieve one or more rewards associated with the one or more inter-AS links.

Optimizing Border Gateway Protocol (BGP) traffic using reinforcement learning

Systems, methods, and computer-readable media are provided for recommending actions to be taken in a network for optimizing or improving the operability of the network. A method, according to one implementation, includes a first step of receiving raw, unprocessed data that is obtained directly from one or more network elements of a network. The method includes second step of determining one or more remedial actions using a direct association between the raw, unprocessed data and the one or more remedial actions.

Action recommendation engine (ARE) for network operations center (NOC) solely from raw un-labeled data

An Artificial Intelligence (AI)-based network control system includes an AI system configured to obtain data from a network having a plurality of network elements and to determine actions for network control through one or more Machine Learning (ML) algorithms; a controller configured to cause the actions in the network; and a safeguard module between the AI system and the controller, wherein the safeguard module is configured to one of allow, block, and modify the actions from the AI system.

Systems and methods for analyzing the root cause of service failures and service degradation in a telecommunications network are provided. A method, according to one implementation, includes a step of receiving any of Performance Monitoring (PM) data, standard path alarms, service PM data, standard service alarms, network topology information, and configuration logs from equipment configured to provide services in a network. The method also includes a step of automatically detecting a root cause of a service failure or signal degradation from the available PM data, standard path alarms, service PM data, standard service alarms, network topology information, and configuration logs.

Systems and methods for detecting patterns in data from a time-series are provided. According to some implementations, the systems and methods may use network topology information combined with object recognition techniques to detect patterns. One embodiment of a method includes the steps of obtaining information defining a topology of a multi-layer network having a plurality of Network Elements (NEs) and a plurality of links interconnecting the NEs and receiving Performance Monitoring (PM) metrics and one or more alarms from the multi-layer network. Based on the information defining the topology, the PM metrics, and the one or more alarms, the method also includes the step of utilizing a Machine Learning (ML) process to identify a problematic component from the plurality of NEs and links and to identify a root cause associated with the problematic component.

Identifying and locating a root cause of issues in a network having a known topology

Systems and methods for recommending actions in a closed-loop system are provided. In one embodiment, an Action Recommendation Engine (ARE) may include a processor and memory configured to store computer programs having instructions that cause the processor to obtain input data pertaining to a state of a network and obtain information regarding one or more historical actions performed on the network. Also, the instructions may cause the processor to utilize a Machine Learning (ML) model for imposing one or more current actions on the network, the one or more current actions selected from the group of procedures consisting of: a) suggesting one or more remediation actions that, when performed, transition the network from a problematic state to a normal state, and b) identifying one or more root causes in response to detecting a transition in the network from a normal state to a problematic state.

Action recommendation engine (ARE) of a closed-loop machine learning (ML) system for controlling a network

Systems and methods implemented by a computer to detect abnormal behavior in a network include obtaining Performance Monitoring (PM) data including one or more of production PM data, lab PM data, and simulated PM data; determining a model based on machine learning training with the PM data; receiving live PM data from the network; utilizing the live PM data with the model to detect an anomaly in the network; and causing an action to address the anomaly.

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Gatineau, Canada

David Côté