Abstract: The present disclosure relates to systems and methods for ML model feature selection and transformation. Specifically, the system and method Include receiving information and data from a network having resources; implementing feature selection on one or more network Machine Learning (ML) models, such that each is a pipeline of a plurality of functions to control the resources and with specified interfaces to other control applications; utilizing one or more feature graph engines (FGEs) which creates one or more feature graphs, from the information and data, as a functional component to derive a design-time set of feature vector for a specific context, each feature graph represents network layer representations in the network which includes multiple layers; and implementing changes to the one or more feature graphs based on any run-time updates from the pipeline.

Abstract: The present disclosure relates to systems and methods for ML model feature selection and transformation. Specifically, the system and method Include receiving information and data from a network having resources; implementing feature selection on one or more network Machine Learning (ML) models, such that each is a pipeline of a plurality of functions to control the resources and with specified interfaces to other control applications; utilizing one or more feature graph engines (FGEs) which creates one or more feature graphs, from the information and data, as a functional component to derive a design-time set of feature vector for a specific context, each feature graph represents network layer representations in the network which includes multiple layers; and implementing changes to the one or more feature graphs based on any run-time updates from the pipeline.

Abstract: Artificial Intelligence (AI)-based network control includes obtaining data from a network having a plurality of network elements; analyzing the data with one or more Machine Learning (ML) algorithms to determine one or more actions for network control; analyzing the determined one or more actions to determine any risks associated therewith; and one of allowing, modifying, and blocking the determined one or more actions based on the determined risks to safeguard the network. The risks can be based on one or more of (1) non-deterministic behavior AI inference which is statistical in nature, (2) unbounded uncertainty of the AI inference that can result in arbitrarily large inaccuracy on rare occasions, (3) unpredictable behavior of the AI inference in presence of input data that is different than data in training and testing datasets, and (4) malicious input data.

Abstract: Systems and methods include receiving information from a governed system having resources; implementing one or more context neutral or specific control applications that each is a pipeline of a plurality of functions to control the resources; utilizing the information as one input to a first function in the plurality of functions; utilizing inputs from one or more control applications as inputs to any of the plurality of functions; and causing actions on the resources based on outputs of a last function in the plurality of functions. The plurality of functions can include sensing, discerning, inferring, deciding, and causing the actions.

Abstract: Artificial Intelligence (AI)-based network control includes obtaining data from a network having a plurality of network elements; analyzing the data with one or more Machine Learning (ML) algorithms to determine one or more actions for network control; analyzing the determined one or more actions to determine any risks associated therewith; and one of allowing, modifying, and blocking the determined one or more actions based on the determined risks to safeguard the network. The risks can be based on one or more of (1) non-deterministic behavior AI inference which is statistical in nature, (2) unbounded uncertainty of the AI inference that can result in arbitrarily large inaccuracy on rare occasions, (3) unpredictable behavior of the AI inference in presence of input data that is different than data in training and testing datasets, and (4) malicious input data.

Abstract: Systems and methods include receiving information from a governed system having resources; implementing one or more context neutral or specific control applications that each is a pipeline of a plurality of functions to control the resources; utilizing the information as one input to a first function in the plurality of functions; utilizing inputs from one or more control applications as inputs to any of the plurality of functions; and causing actions on the resources based on outputs of a last function in the plurality of functions. The plurality of functions can include sensing, discerning, inferring, deciding, and causing the actions.

Abstract: An Artificial Intelligence (AI)-based network control system includes an AI system configured to obtain data from a network having a plurality of network elements and to determine actions for network control through one or more Machine Learning (ML) algorithms; a controller configured to cause the actions in the network; and a safeguard module between the AI system and the controller, wherein the safeguard module is configured to one of allow, block, and modify the actions from the AI system.

Abstract: An Artificial Intelligence (AI)-based network control system includes an AI system configured to obtain data from a network having a plurality of network elements and to determine actions for network control through one or more Machine Learning (ML) algorithms; a controller configured to cause the actions in the network; and a safeguard module between the AI system and the controller, wherein the safeguard module is configured to one of allow, block, and modify the actions from the AI system.

Abstract: A dynamic registration system includes a slice registration server communicatively coupled to one or more clients, and to one or more Application Programming Interfaces (APIs), wherein each API is communicatively coupled to an associated network of one or more networks, each network having resources including one or more of transport, compute, and storage resources; wherein the slice registration system is configured to receive requests for resources of the one or more of transport, compute, and storage resources in the one or more networks, for a client, exchange request/response messages with the one or more networks for a slice registration of the resources in the one or more networks, cause instantiation of the resources in the one or more networks, based on the request/response messages, and provide an acknowledgment to the client based on the instantiation of the resources.

Abstract: A method, implemented by a slice registration server, for dynamic reservation of network slices includes receiving a first request from a client for a network slice including one or more of networks, compute, and storage resources in one or more networks for a time period; determining availability of the network resources based on the first request and a state of the one or more networks; transmitting a response to the client based on the availability; receiving a second request from the client based on the response; and causing instantiation of the network resources and providing an acknowledgment to the client based thereon.

Abstract: A method, implemented by a slice registration server, for dynamic reservation of network slices includes receiving a first request from a client for a network slice including one or more of networks, compute, and storage resources in one or more networks for a time period; determining availability of the network resources based on the first request and a state of the one or more networks; transmitting a response to the client based on the availability; receiving a second request from the client based on the response; and causing instantiation of the network resources and providing an acknowledgment to the client based thereon.

Abstract: Systems and methods for path computation in an optical network include obtaining optical layer characteristics related to one or more optical paths in the optical network based in part on performance measurements in the optical network; responsive to service establishment or service restoration, determining a path from source to destination based on utilizing the optical layer characteristics to confirm physical validity of the path; and provisioning a service on the determined path from the source to the destination in the optical network.

Abstract: Systems and methods for path computation in an optical network include obtaining optical layer characteristics related to one or more optical paths in the optical network based in part on performance measurements in the optical network; responsive to service establishment or service restoration, determining a path from source to destination based on utilizing the optical layer characteristics to confirm physical validity of the path; and provisioning a service on the determined path from the source to the destination in the optical network.

Abstract: The present disclosure provides dynamic performance monitoring systems and methods for optical networks to ascertain optical network health in a flexible and accurate manner. The present invention introduces accurate estimations for optical channel performance characteristics based either on existing channels or with a dynamic optical probe configured to measure characteristics on unequipped wavelengths. Advantageously, the dynamic performance monitoring systems and methods introduce the ability to determine physical layer viability in addition to logical layer viability.

Abstract: The present disclosure provides dynamic performance monitoring systems and methods for optical networks to ascertain optical network health in a flexible and accurate manner. The present invention introduces accurate estimations for optical channel performance characteristics based either on existing channels or with a dynamic optical probe configured to measure characteristics on unequipped wavelengths. Advantageously, the dynamic performance monitoring systems and methods introduce the ability to determine physical layer viability in addition to logical layer viability.

Abstract: Software Defined Networking systems and methods are described via a Path Computation and Control Element (PCCE) that is based in part on a Path Computation Element (PCE). A common, simple interface is designed based on an existing PCE interface that allows a centralized entity (i.e., a Path Computation and Control Element or PCCE) to control the initiation of new connections or tunnels and by default to manage the state of these connections or tunnels once established. In particular, the systems and methods create an extension to the PCE architecture to allow a centralized application or applications to control the creation, rerouting and deletion of connections within a network.

Abstract: The present disclosure provides dynamic performance monitoring systems and methods for optical networks to ascertain optical network health in a flexible and accurate manner. The present invention introduces accurate estimations for optical channel performance characteristics based either on existing channels or with a dynamic optical probe configured to measure characteristics on unequipped wavelengths. Advantageously, the dynamic performance monitoring systems and methods introduce the ability to determine physical layer viability in addition to logical layer viability.

Abstract: A method and system for path computation in a communications network having multiple domains are disclosed. According to one aspect, a method of path computation across multiple domains includes identifying a plurality of border nodes at borders of a plurality of domains, each domain having at least one border node. The method includes providing a path computation element at each of the plurality of border nodes of the domains of the network. The locations of the path computation elements are known to be at the border nodes prior to determining a path in response to a path computation request.

Abstract: Software Defined Networking systems and methods are described via a Path Computation and Control Element (PCCE) that is based in part on a Path Computation Element (PCE). A common, simple interface is designed based on an existing PCE interface that allows a centralized entity (i.e., a Path Computation and Control Element or PCCE) to control the initiation of new connections or tunnels and by default to manage the state of these connections or tunnels once established. In particular, the systems and methods create an extension to the PCE architecture to allow a centralized application or applications to control the creation, rerouting and deletion of connections within a network.

Abstract: Software Defined Networking systems and methods are described via a Path Computation and Control Element (PCCE) that is based in part on a Path Computation Element (PCE). A common, simple interface is designed based on an existing PCE interface that allows a centralized entity (i.e., a Path Computation and Control Element or PCCE) to control the initiation of new connections or tunnels and by default to manage the state of these connections or tunnels once established. In particular, the systems and methods create an extension to the PCE architecture to allow a centralized application or applications to control the creation, rerouting and deletion of connections within a network.