Ciena Corporation

Christopher Barber

David Côté

Thomas Triplet

Yinqing Pei

Shervin Shirmohammadi

Sa'di Altamimi

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 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.

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Ottawa, Canada

Christopher Barber