Techniques for providing a non-blocking fabric in a network are described. A network controller determines the network requirement for various network traffic types on the network and determines the allocation of resources across the network needed to establish a midlay, including midlay components on the network. The network controller then establishes the midlay on the network according to the determined allocation. At least one of the midlay components is a virtually non-blocking fabric for high-priority traffic or fully non-blocking fabric for deterministic traffic.

The disclosed computer-implemented method includes accessing information related to a playback session in which at least a portion of requested multimedia content is streamed over a network to a client electronic device. The method further includes accessing network topology information for the network to identify which route through the network was used to provide the requested multimedia content during the playback session, including indicating which end node was used to provide the multimedia content. Still further, the method includes accessing network steering factors that indicate why the requested multimedia content was steered through the identified network route, determining, based on the network steering factors, which end node would have been more suited to providing the requested multimedia content for the playback session, and then transferring the requested multimedia content to the determined end node for provisioning during subsequent playback sessions. Various other methods, systems, and computer-readable media are also disclosed.

Disclosed are examples of systems, apparatus, devices, computer program products, and methods implementing aspects of a decentralized content fabric. In some implementations, one or more processors are configured to provide fabric nodes of an overlay network, including one or more fabric nodes that receive a client's request to access digital content on the overlay network. The request includes an authorization token digitally signed by or on behalf of a user of the client. The fabric node(s) extract a user identifier (ID) from the authorization token, then determine that one or more rules maintained on the overlay network are satisfied. The one or more rules condition access to the digital content upon the extracted user ID matching an ID associated with an owner of a digital instrument. The digital instrument, which can be a non-fungible token, is stored in a blockchain ledger as a unique representation of the digital content.

A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, perform certain acts. The acts can include receiving a deployment model selection of a software-defined-network (SDN) control service. The deployment model selection includes one of a centralized model, a decentralized model, a distributed model, or a hybrid model. The acts also can include deploying the SDN control service in the deployment model selection to control a physical computer network. The SDN control service uses a routing agent model trained using a reinforcement-learning model. Other embodiments are described.

A network analysis device that is configured to obtain metric information that is associated with a plurality of messages and to input the metric information into a first machine learning model that outputs a traffic volume classification based on the metric information. The network analysis device is further configured to obtain bandwidth information that is associated with a plurality of network devices and to input the bandwidth information and the traffic volume classification into a second machine learning model that outputs routing recommendations based on the bandwidth information and the traffic volume classification. The network analysis device is further configured to generate routing instructions based on the routing recommendations and to reconfigure a routing device based on the routing instructions.

Methods, systems, and apparatus, including computer-readable storage media, optimizing interior gateway protocol (IGP) metrics using reinforcement learning (RL) for a network domain. The system can receive a topology (G) of a network domain, a set of flows (F), and an objective function. The system can optimize, using reinforcement learning, the objective function based on the received topology and the one or more flows F. The system can determine updated IGP metrics based on the optimization of the objective function. The IGP metrics for the metric domain may be updated with the updated IGP metrics.

The disclosure provides a networked computing system, comprising at least one network communication interface connected to at least one network, the at least one network communication interface being configured to receive data from and to send data to the at least one network, a control component, wherein the control component is adapted to configure routes, wherein the control component is configured to provide current input parameters on the routes, and wherein an application component is configured to output predicted configuration parameters for future route configurations based on predictions, based on the predicted configuration parameters output by the application component.

Edge networking router devices and systems for identifying a software application are described herein. One or more embodiments include an edge networking router device for identifying a software application comprising a packet collector to receive packet data in the edge networking router device and an artificial intelligence (AI) model configured to process the packet data received by the packet collector to identify the software application, wherein the artificial intelligence (AI) model is trained using a cloud entity and received from the cloud entity.

A node mesh contains an originating node and several node groups, each node group consisting of one or more nodes with interfaces connected to other nodes of the node group. Each node of a node group has an associated route table with an association between an applied DC voltage and an output interface to couple the input signal to. When the originating node outputs a DC voltage accompanied by differential signaling, each node in turn directs the DC voltage and differential signaling to an output interface as directed by the node local route table to a local termination in a node, which may be coupled to a training processor of inference processor for machine learning applications.

A system and a method of optimizing an organizational structure (OS) of an organization may include: receiving one or more data elements pertaining to the OS; receiving a value of one or more transaction parameters pertaining to one or more transactions conducted over one or more nodes of a first computer network; perturbating a value of one or more OS elements; creating a simulated computer network based on the one or more perturbated values; for each network of the first computer network and the simulated computer network, calculating a value of at least one OS performance parameter; and generating, based on the calculation, a suggestion for optimizing the OS, wherein the suggestion may include at least one perturbated OS element value.