Ciena Corporation

Olivier Simard

David Côté

Thomas Triplet

Emil Janulewicz

Gauravdeep Singh Shami

Merlin Davies

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

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 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.

Systems and methods include obtaining performance monitoring data from a network which operates and generates the performance monitoring data which reflects a current state of the network; responsive to a predetermined reward which quantifies minimizing or maximizing an aspect in the network for Reinforcement Learning, causing one or more actions to be performed in the network, wherein the one or more actions are based on the predetermined reward; obtaining updated performance monitoring data from the network subsequent to the one or more actions to determine an updated state of the network; and continuing the causing the one or more actions to be performed in the network based on the current state and the updated state. The causing the one or more actions to be performed in the network is performed to maximize or minimize a total discounted accumulated reward.

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Montréal, Canada

Olivier Simard