Ciena Corporation

Shelley A Bhalla

David Côté

Thomas Triplet

Emil Janulewicz

Cengiz Alaettinoglu

Robert Kevin Tomkins

Shervin Shirmohammadi

Shady A Mohammed

Magdi Wissa

Ayse Rumeysa Mohammed

Systems and methods include obtaining overlay telemetry data from a plurality of overlays, wherein each overlay is an application and there is a corresponding telemetry adaptor for each overlay; obtaining underlay telemetry data from one or more underlays, wherein each underlay includes physical infrastructure for supporting one or more of network, compute, and store functions for the plurality of overlays; analyzing the overlay telemetry data and the underlay telemetry data via a Key Performance Factor (KPF) model that correlates one or more of the plurality of overlays and the one or more underlays together; responsive to an anomaly or a threshold crossing based on the KPF model, performing a Root Cause Analysis (RCA) to identify a root cause of the anomaly or the threshold crossing; and mapping one or more actions with associated priorities to the root cause utilizing one or more remediation modules each associated with an overlay.

Systems and methods for precisely generalized and modular underlay/overlay service and experience assurance

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 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 include obtaining telemetry data associated with a network having a plurality of network elements; presenting a list of ongoing issues and predicted issues based on the telemetry data, on a display; responsive to a selection of an issue that is one of the ongoing issues and the predicted issues in the list, presenting a root cause analysis of the issue including one or more diagnosis; presenting a list of prescriptive actions on the display to address the issue based on the root cause analysis including a mechanism for a user to cause execution of any of the prescriptive actions; and receiving a selection of one or more of the prescriptive actions from the user.

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Morrisville, NC, United States of America

Shelley A Bhalla