An object of the present disclosure is to enable reduction of network congestion and concentration of communication on a server side that locally occur at the time of a disaster, an event, or the like and to enable important communication. A communication network control system according to the present disclosure includes: a plurality of communication control devices (20, 30) that control communication of apparatuses (92, 93); and a central communication control device (10) that distributes communication control information to the plurality of communication control devices, the central communication control device (10) collects event information linked to position information, determines an area with a probability that the amount of communication increases, using the collected event information, in a case in which there is an area with the probability that the amount of communication increases, generates communication control information that places a priority on communication in the area, and distributes the generated communication control information to the plurality of communication control devices (20, 30).

An object of the present disclosure is to enable reduction of network congestion and concentration of communication on a server side that locally occur at the time of a disaster, an event, or the like and to enable important communication. A communication network control system according to the present disclosure includes: a plurality of communication control devices (20, 30) that control communication of apparatuses (92, 93); and a central communication control device (10) that distributes communication control information to the plurality of communication control devices, the central communication control device (10) collects event information linked to position information, determines an area with a probability that the amount of communication increases, using the collected event information, in a case in which there is an area with the probability that the amount of communication increases, generates communication control information that places a priority on communication in the area, and distributes the generated communication control information to the plurality of communication control devices (20, 30).

A system and method for predictive congestion detection for network devices is provided. A routing engine associated with an input of a router receives congestion information from an output, utilizing the received congestion information to initialize a congestion value associated with that output. Between receipt of updated congestion information from the output, the routing engine predicts a potential change in the congestion state at the output based on the congestion value and information regarding usage of the output that is known to the routing engine.

A system and method for predictive congestion detection for network devices is provided. A routing engine associated with an input of a router receives congestion information from an output, utilizing the received congestion information to initialize a congestion value associated with that output. Between receipt of updated congestion information from the output, the routing engine predicts a potential change in the congestion state at the output based on the congestion value and information regarding usage of the output that is known to the routing engine.

Embodiments are directed towards systems and methods for user plane function (UPF) and network slice load balancing within a 5G network. Example embodiments include systems and methods for load balancing based on current UPF load and thresholds that depend on UPF capacity; UPF load balancing using predicted throughput of new UE on the network based on network data analytics; UPF load balancing based on special considerations for low latency traffic; UPF load balancing supporting multiple slices, maintaining several load-thresholds for each UPF and each slice depending on the UPF and network slice capacity; and UPF load balancing using predicted central processing unit (CPU) utilization and/or predicted memory utilization of new UE on the network based on network data analytics.

Embodiments are directed towards systems and methods for user plane function (UPF) and network slice load balancing within a 5G network. Example embodiments include systems and methods for load balancing based on current UPF load and thresholds that depend on UPF capacity; UPF load balancing using predicted throughput of new UE on the network based on network data analytics; UPF load balancing based on special considerations for low latency traffic; UPF load balancing supporting multiple slices, maintaining several load-thresholds for each UPF and each slice depending on the UPF and network slice capacity; and UPF load balancing using predicted central processing unit (CPU) utilization and/or predicted memory utilization of new UE on the network based on network data analytics.

Systems and methods are provided for monitoring traffic flow using a trained machine learning (ML) model. For example, in order to maintain a stable level of connectivity and network experience for the devices in a network, the ML model can monitor the data flow of each device and label each data flow based on its behavior and properties. The system can take various actions based on the labeled data flow, including generate an alert, automatically change network settings, or otherwise adjust the data flow from the device.

Systems and methods are provided for monitoring traffic flow using a trained machine learning (ML) model. For example, in order to maintain a stable level of connectivity and network experience for the devices in a network, the ML model can monitor the data flow of each device and label each data flow based on its behavior and properties. The system can take various actions based on the labeled data flow, including generate an alert, automatically change network settings, or otherwise adjust the data flow from the device.

A restoration determination device 1 calculates, based on past traffic data of each user in a first NW device, a current estimated traffic amount of the user, compares the calculated current estimated traffic amount of the user with a current traffic amount of the user in a second NW device to which the first NW device is switched, and determines restoration by switching to the second NW device to be abnormal when the number of users for which the current estimated traffic amount is larger than zero but the current traffic amount is zero exceeds a threshold value.

A restoration determination device 1 calculates, based on past traffic data of each user in a first NW device, a current estimated traffic amount of the user, compares the calculated current estimated traffic amount of the user with a current traffic amount of the user in a second NW device to which the first NW device is switched, and determines restoration by switching to the second NW device to be abnormal when the number of users for which the current estimated traffic amount is larger than zero but the current traffic amount is zero exceeds a threshold value.