Ciena Corporation

Bruck Wubete

Thomas Triplet

Arslan Shahid

David Côté

Babak Esfandiari

Saurabh Dinesh Brahmankar

Sudhan Puranik

Dirk Tempel

Thomas Kunz

Yogeshwar Chatur Deore

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 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

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

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Nepean, Canada

Bruck Wubete