Ciena Corporation

Thomas Triplet

David Côté

Emil Janulewicz

Arslan Shahid

Olivier Simard

Shelley A Bhalla

Merlin Davies

Bruck Wubete

Lyndon Y Ong

Raghuraman Ranganathan

Gauravdeep Singh Shami

Yan Liu

Saurabh Dinesh Brahmankar

Sudhan Puranik

Michael Y Frankel

Yang Yu

Petar Djukic

Todd D Morris

Cengiz Alaettinoglu

Yinqing Pei

Qunbi Zhuge

Shervin Shirmohammadi

Babak Esfandiari

Christopher Barber

Shady A Mohammed

Patrick Premont

Asit Panigrahy

Vidya Sivaraju

Phillip Doelling

Ayse Rumeysa Mohammed

Kevin Kim

Vandana Putchala

Hitendri Bomble

Paul Gosse

Yogeshwar Chatur Deore

Thomas Kunz

Magdi Wissa

Dana Dennis

Dirk Tempel

Maheshwaran Venkatachalam

Shelly A Bhalla

Systems and methods are provided herein for analyzing root-causes of network access failures in a wireless network. In response to detecting that a client device experiences a network access failure that prevents communication with a server device, a method, according to one implementation, includes a step of analyzing the network access failure to predict one or more root-causes. Also, the method includes beginning a remediation procedure for remediating the one or more root-causes.

Determining a root-cause of a network access failure and conducting remediation

Systems and methods include obtaining input data related to a networking system; modeling operation of the networking system via a composite model that includes a plurality of sub-models that collectively form the composite model, wherein at least one sub-model is a behavioral sub-model and at least one sub-model is a machine learning model, wherein various data in the input data is provided to corresponding sub-models, and wherein each sub-model is configured to model one or more components in the networking system; and providing output data based on the modeling.

Network system modeling using nested models combining machine learning and behavioral approaches

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

Systems and methods for analyzing and prioritizing alarms in a communications network are provided. A method, according to one implementation, includes the step of obtaining network information regarding the condition of a network. Using the network information, the method further includes performing a hybrid Machine Learning (ML) technique that includes training and inference of a plurality of ML models to calculate metrics of the network. Also, the method includes the step of selecting one of the plurality of ML models based on a combination of the metrics.

Switching among multiple machine learning models during training and inference

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 and methods for analyzing root-causes of Wi-Fi issues in a Wi-Fi system associated with a Local Area Network (LAN) are described in the present disclosure. A method, according to one embodiment, includes a step of monitoring a Wi-Fi system associated with a LAN to detect authentication failures in the Wi-Fi system. In response to detecting an authentication failure in the Wi-Fi system, the method also includes the step of analyzing the authentication failure to determine one or more root-causes of the authentication failure. The method also includes pushing changes to the Wi-Fi system to automatically remediate the one or more root-causes in the Wi-Fi system.

Root-cause analysis and automated remediation for Wi-Fi authentication failures

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 a root cause of issues in a network, such as an optical communication network, are provided. A method, according to one implementation, includes the steps of deriving symptoms indicative of issues in a network by utilizing performance data obtained from network elements in the network and storing the derived symptoms in a database. The method also includes the step of obtaining diagnostics from the network elements. Also, the method may include utilizing the database to compute distances between the derived symptoms and each of the diagnostics, whereby the computed distances may be configured to correspond to dissimilarities between the derived symptoms and the diagnostics. The distances are computed based on machine learning models, user feedback, and analytical functions. Also, the method includes analyzing a root cause of the derived symptoms based on a lowest distance selected from the computed distances.

Knowledge base and mining for effective root-cause analysis

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.

Growing community of inventors

Manotick, Canada

Thomas Triplet