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.

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 execute a software stack to define a fabric node of a plurality of fabric nodes of an overlay network situated in an application layer differentiated from an internet protocol layer. The defined fabric node is configured to: obtain a request for digital content from a client device; obtain, from one or more of the plurality of fabric nodes, a plurality of content object parts of a content object representing, in the overlay network, at least a portion of the digital content; generate consumable media using: raw data stored in the content object parts, metadata stored in the content object parts, and build instructions stored in the content object parts; and provide the consumable media to the client device. In some instances, the consumable media is further generated using a digital contract stored in a blockchain.

A method, processing system and processor-readable medium for generating network configurations using a graph neural network (GNN) are provided. The method may include receiving a first matrix M generated based on a set of network requirements; storing a GNN having a plurality of nodes v and a plurality of edges; initializing the GNN based on a second matrix X.sub.v having a plurality of elements, each element corresponding to a node from a plurality of nodes v of the GNN; and generating an output matrix having a plurality of nodes labelled based on the first matrix M.

A node mesh contains an originating node coupled to one or more nodes, each node having an communications interface input and a communications interface output. Each node has a route table with an association between a header amplitude and an output interface, such that a header having a particular amplitude causes the input node which received the message to couple the message to an associated communications interface output of the node. When the originating node outputs a message with a header amplitude, each node of the node mesh in turn directs the message to an output interface as directed by the node local route table to a terminating node of the node mesh, where the terminating node may be a training processor or inference processor for machine learning applications.

In one embodiment, a device obtains metadata for routing decisions made by a predictive routing service for a plurality of network deployments. The device identifies a network topology for a network deployment that does not use the predictive routing service. The device estimates, based on the metadata for routing decisions made by the predictive routing service, performance metrics for the predictive routing service were it to be used to make routing decisions for the network topology. The device sends, to a user interface, report data indicative of the performance metrics estimated for the predictive routing service were it to be used to make routing decisions for the network topology.

A system and method for mobile device active callback integration, utilizing a callback integration engine operating on a user's mobile device that present a callback token for integration through the operating system and software applications operating on the device, wherein interacting with the callback token produces a callback object used to execute a callback incorporating device hardware, context, scheduling, and trust information.

Various example embodiments for supporting multi-tier deterministic networking are presented. Various example embodiments for supporting multi-tier deterministic networking may be configured to support provisioning of deterministic flows in multi-tier deterministic networking. Various example embodiments for supporting multi-tier deterministic networking may be configured to support adaptive deterministic routing in multi-tier deterministic networks. Various example embodiments for supporting multi-tier deterministic networking may be configured to support score-based deterministic routing in multi-tier deterministic networks. Various example embodiments for supporting multi-tier deterministic networking may be configured to support adaptive deterministic routing and/or score-based deterministic routing in multi-tier deterministic networks based on analysis of a state representation for path and/or sub-path selection in multi-tier deterministic networks. Various example embodiments for supporting multi-tier deterministic networking may be configured to support hierarchical resource allocation and deallocation in multi-tier deterministic networking, optimal route finding in multi-tier deterministic networking, and so forth.

A system and method for mobile device active callback integration, utilizing a callback integration engine operating on a user's mobile device that present a callback token for integration through the operating system and software applications operating on the device, wherein interacting with the callback token produces a callback object used to execute a callback incorporating device hardware, context, scheduling, and trust information.

Systems and methods are provided for detecting changes in network activity that are depicted in a routing table. The routing table may be stored as a search tree data structure (e.g., Merkle Patricia Tree) to mimic a standard routing table and reduce the search time to find the desired route by allowing the router to traverse the search tree data structure more efficiently. Additionally, the metadata of the tree may be provided to an unstructured machine learning model (e.g., K-means) to identify new clusters of routes week-over-week and generate an alert with any changes. Changes are identified in near real time and dynamically at the router (not a central device) to reduce the time needed to respond to network changes.

A system and method for mobile device multitenant active and ambient callback prioritization, utilizing a callback integration engine to generate callback lists for multiple tenants, an environment analyzer, and a prioritization engine operating on a user's mobile device for integration through the operating system and software applications operating on the device, wherein the environment analyzer retrieves and aggregates ambient data related to the mobile device, inputs the aggregated ambient data into one or more machine learning algorithms wherein the algorithms may analyze the input data, the results of the analysis may be used to compute whether a user is available to be prioritized to receive a callback.