Ciena Corporation

Sergio Slobodrian

William Webb

David Miedema

Kaniz A Mahdi

David Côté

Stephen B Alexander

Nigel Robert Davis

Emil Janulewicz

David Jordan Krauss

Norman Steven Traxler

Mahdi Hirab

In various embodiments, the present disclosure relates to systems and methods for enhanced reinforcement learning (RL) algorithms using future state prediction. In some embodiments, an offline emulator can be applied allowing the generation of samples, thus supporting continuous training of the system and fast-forward fabric saturation. The fabric accepts transactions which allocate resources with respect to the transactions needs and constraints and contains an RL/AI model(s) which are continuously learning based on the current reward combined with reward scaling. By modelling the fabric and transactions in an emulator, it is possible to predict future states and calculate adjusted rewards with respect to the optimal criterion. A state generator is based on modeling past historical transactions, allowing a user to anticipate future state characteristics of the fabric. In some embodiments, online learning is based on adjusted rewards which are more representative with respect to the objective function.

Enhanced reinforcement learning algorithms using future state prediction scaled reward values

A controller associated with a domain includes a network interface; one or more processors communicatively coupled to the network interface; and memory storing instructions that, when executed, cause the one or more processors to communicate with one or more additional controllers via the network interface, wherein each of the one or more additional controllers is in one or more additional domains, and wherein each domain provides different characteristics, utilize at least part of a control pattern to obtain requirements for a service, and cause, utilizing any of a peer relationship and a hierarchical relationship with the one or more additional controllers, at least part of implementation of a composition of resources to meet the requirements for the service, wherein the composition defines the resources provided in each domain for the service, and wherein the composition is based on the requirements and the different characteristics in each domain.

Self-optimizing fabric architecture and self-assembling network

A bandwidth control method implemented in a Software Defined Networking (SDN) network includes obtaining data for one or more services in the network, wherein each of the one or more services is controlled by an associated user-agent; determining future bandwidth requirements for the one or more services based on the data; determining service requests for at least one of the one or more services based on the future bandwidth requirements; and causing implementation of at least one of the service requests in order of priority. The process of prioritization uses a programmable network-wide logic and has the ability to consider information external to the network such as a user's Service Layer Agreement (SLA) and business priority. The entire bandwidth control method can repeat in cycles providing near real-time adjustments.

Dynamic bandwidth control systems and methods in software defined networking

Path calculation systems and methods for determining a path, based on constraints and rules, for a connection at one or more layers in a network, include determining a path exploration map of the network based on a multi-layer network model of the network defining external edges and intra-node paths, the path exploration map comprising every external port in the network that is reachable from a source port; and utilizing the path exploration map to determine a viable path, from a destination port to the source port, subject to the constraints and the rules and based on a cost.

Constrained and viable path computation systems and methods

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Ottawa, Canada

Sergio Slobodrian