Ciena Corporation

Magor Communications Corporation

Swarmio Inc.

Shervin Shirmohammadi

David Côté

Shady A Mohammed

Sa'di Altamimi

Thomas Triplet

Emil Janulewicz

Shelley A Bhalla

Christopher Barber

Shady Mohammed

Alaa Eddin Alchalabi

Aziz Khanchi

Mehdi Semsarzadeh

Abbas Javadtalab

Karthigesu Vijayasuganthan

Basel Altamimi

Ayse Rumeysa Mohammed

Sorin Stoian

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 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

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

The techniques described herein relate to methods, apparatus, and computer readable media configured to select a computing resource from a plurality of computing resources to perform a computing process. A request is received from a remote computing device to perform the computing process. A first set of estimated metrics is accessed that includes an estimated metric for each computing resource and the first remote computing device. The second data is processed using a machine learning algorithm to select a candidate computing resource to perform the process. The machine learning algorithm selects the candidate computing resource based on a second estimated metric between at least one second remote computing device and an associated computing resource from the plurality of computing resources performing a second computing process for the at least one second remote computing device, and a capacity of each computing resource of the plurality of computing resources.

Methods and apparatus for network delay and distance estimation, computing resource selection, and related techniques

To control the transmission of real time transport packets over a network, incoming packets received over the network from a transmitter are monitored. A statistical analysis is performed on the received packets to determine network congestion. A message is sent to the transmitter to control a transmission parameter in accordance with the determined network congestion.

Network congestion prediction

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Ottawa, Canada

Shervin Shirmohammadi