Aspects of the present disclosure involve systems, methods, devices, and the like for generating highly scalable temporal graph databases. In one embodiment, a novel architecture is presented that enables the identification of mutation or changes in graphs. For the identification a combination of graph-based modeling and journal entry is used. Events occurring are consumed and changes are ingested, transformed for use by a graph simulation system. The changes are journaled using a vertex centric temporal journaling schema.

An information processing apparatus is communicable with an electronic apparatus according to a plurality of protocols. The information processing apparatus includes circuitry. The circuitry identifies a particular protocol supported by the electronic apparatus among the plurality of protocols. The circuitry determines a management type based on the identified particular protocol. The circuitry sets a communication path through which the electronic apparatus communicates with a management apparatus based on the determined management type.

A system and method for dynamically creating distributed, self-adjusting and optimizing core network with machine learning is disclosed. The method includes receiving a request to access one or more services and establishing a secure real time communication session with one or more client devices and a set of service layers based on the received request. The method further includes determining one or more service parameters based on the received request and sending one or more handshake messages to each of the set of service layers. Further, the method includes determining one or more environmental parameters and determining best possible service layer capable of processing the received request by using a trained service based ML model. The method includes processing the request at the determined best possible service layer and terminating or transferring the secure real time communication session after the request is processed.

It is an objective of the present disclosure to reduce computational complexity for obtaining a network topology configuration capable of efficiently accommodating a plurality of traffic demands assumed between nodes present in a network.

A device of the present disclosure includes: a computation traffic generation unit that creates a traffic demand matrix whose elements are values individually obtained by demands between nodes present in a target network area from a plurality of traffic demand matrices created based on predicted traffic demands between the nodes; and a network topology generation unit that generates a network topology based on the traffic demand matrix generated by the computation traffic generation unit and port information of the nodes in the network area.

Methods are provided for modifying assurance monitoring of a service based on operational states. The methods involve establishing, based on service configuration information, an assurance monitoring for a service provided by a plurality of network nodes that establish network connectivity for the service. The service includes a plurality of sub-services. The methods further involve obtaining, from the plurality of network nodes, telemetry data related to the service, determining one or more operational states of the plurality of network nodes based on the telemetry data, and modifying the assurance monitoring for the service based on the one or more operational states of the plurality of network nodes.

A topology design device for an optical transmission network includes a memory; and a processor to determine a bypass target area as an area to which a bypass route is to be added in the network, the area indicating a region surrounded by nodes and edges, based on an area demand, namely a total value of a demand for communication to be performed via an edge, by referencing a topology management database storing information regarding nodes, edges, and areas in the network, and a demand management database storing demands in the network; and exclude a node from nodes constituting the bypass target area and determining a bypass route from the nodes from which the node has been excluded, based on a node demand, namely a total value of a demand for which each node serves as a start or end point of the demand, by referencing the databases.

The invention relates to a system for monitoring and controlling a dynamic network such as an oil, gas, or water pipeline. The system includes a plurality of sensors for measuring aspects of a state of the network with each sensor being associated with a segment of the network and connected to a virtual sensor which accumulates and pre-processes measurements from the sensors for each segment of the network. The system further includes a network topology processor for storing the topology of the network and relating sensors and virtual sensors to segments of the network and neighbouring sensors and virtual sensors in accordance with the topology and a reinforcement learning artificial neural network (ANN) based nonlinear state estimation and predictive control model which uses measurements from the sensors and virtual sensors to model the state of the network and estimate sequential states of the network.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining a terminal idle time. In some implementations, a server can obtain communication data from a plurality of devices in a communication network, wherein the communication data indicates levels of network traffic for the device over time. The server can generate an idle period forecasting model configured to predict occurrence of future communication idle periods in which communication activity is predicted to be below a threshold. The server can provide the idle period forecasting model to each of the plurality of devices such that the devices can respectively use the idle period forecasting model to locally predict future communication idle periods of the devices.

Service assurance is provided. A low priority pod corresponding to a low priority service in an orchestration platform that is to be evicted due to a predicted peak load period of a high priority service is identified based on analysis of historical and resource information. The low priority service corresponding to the low priority pod that is to be evicted due to the predicted peak load period of the high priority service is marked as an assured service for a guaranteed run in response to receiving an input from a user who was notified regarding eviction of the low priority pod. The low priority pod corresponding to the low priority service that is to be evicted due to the predicted peak load period of the high priority service is provisioned on a second host node prior to the eviction of the low priority pod from a first host node.

The described technology is generally directed towards reducing latency in a wireless communications network. Radio access network latency data corresponding to a measured latency impact criterion is obtained by a network device of a wireless network. Based on the radio access network latency data, latency guidance data usable by the radio network device to achieve a reduction in communication latency that is experienced by a user equipment is predicted, e.g., by a learned model. The latency guidance data can be used to facilitate a reduction in the communication latency that is experienced by a user equipment.