Ciena Corporation

Gauravdeep Singh Shami

David Côté

Thomas Triplet

Emil Janulewicz

Olivier Simard

Marc Lyonnais

Rodney G Wilson

Ziqiang Wang

Abdullah Bittar

Systems and methods are provided for predicting how tasks should be allocated to available network resources. A method, according to one implementation, includes the step of receiving resource utilization metrics from each of a plurality of nodes arranged in a cluster, wherein the resource utilization metrics of each node include information related to utilization of one or more resources of the respective node over a plurality of time periods. The method also includes the step of predicting current and forthcoming resource availability of each node based on patterns of resource utilization over past and current time periods. In response to receiving a job request having multiple workload responsibilities, the method includes the step of allocating the workload responsibilities to one or more nodes based on the predicted current and forthcoming resource availability of each node in order to fulfill the job request.

Predictive resource allocation and scheduling for a distributed workload

Systems and methods for creating and updating data transfer strategies for massive amounts of data are provided. A method, according to one implementation, includes obtaining a first set of parameters pertaining to a data source configured as a first node in a communications system, obtaining a second set of parameters pertaining to a data sink configured as a second node in the communications system, and obtaining a third set of parameters pertaining to a plurality of network elements and links configured along one or more data paths between the data source and data sink. The method also includes the step of automatically creating a high-bandwidth data transfer strategy for transferring a massive amount of data from the data source to the data sink based on the first, second, and third sets of parameters.

Optimizing the transfer of massive amounts of data using AI strategies

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 include obtaining performance monitoring data from a network which operates and generates the performance monitoring data which reflects a current state of the network; responsive to a predetermined reward which quantifies minimizing or maximizing an aspect in the network for Reinforcement Learning, causing one or more actions to be performed in the network, wherein the one or more actions are based on the predetermined reward; obtaining updated performance monitoring data from the network subsequent to the one or more actions to determine an updated state of the network; and continuing the causing the one or more actions to be performed in the network based on the current state and the updated state. The causing the one or more actions to be performed in the network is performed to maximize or minimize a total discounted accumulated reward.

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Ottawa, Canada

Gauravdeep Singh Shami