This disclosure describes systems, devices, and techniques for migrating virtualized resources between the main region and edge locations. Live migration enables virtualized resources to remain operational during migration. Edge locations are typically separated from secure data centers via the Internet, a direct connection, or some other intermediate network. Accordingly, to place virtualized resources within an edge location, the virtualized resources must be migrated over a secure communication tunnel that can protect virtualized resource data during transmission over the intermediate network. The secure communication tunnel may have limited data throughput. To efficiently utilize resources of the secure communication tunnel, and to reduce the impact of migrations on virtualized resource operations, virtualized resource migrations may be carefully scheduled in advance. For instance, virtualized resources may be selectively migrated at times-of-day in which they are likely to be relatively idle, or at times when the communication tunnel is predicted to have sufficient bandwidth.

This disclosure describes systems, devices, and techniques for migrating virtualized resources between the main region and edge locations. Live migration enables virtualized resources to remain operational during migration. Edge locations are typically separated from secure data centers via the Internet, a direct connection, or some other intermediate network. Accordingly, to place virtualized resources within an edge location, the virtualized resources must be migrated over a secure communication tunnel that can protect virtualized resource data during transmission over the intermediate network. The secure communication tunnel may have limited data throughput. To efficiently utilize resources of the secure communication tunnel, and to reduce the impact of migrations on virtualized resource operations, virtualized resource migrations may be carefully scheduled in advance. For instance, virtualized resources may be selectively migrated at times-of-day in which they are likely to be relatively idle, or at times when the communication tunnel is predicted to have sufficient bandwidth.

Approaches presented herein enable deploying a distributed machine learning framework in an edge computing environment. More specifically, a status of a connection between a computing system and an edge node of a plurality of edge nodes is monitored. At least one server node and a group of worker nodes from the plurality of edge nodes are identified based on the status. A path for distributing the training data to the worker nodes is determined based on the status. The training data from the edge node to the worker nodes is distributed via the path.

A computer-implemented method upgrades an edge network based on analysis by a learning model. The method includes identifying, in a network, a plurality of devices, where each device in the network is configured to provide data on at least one other device in the network. The method also includes determining capabilities of each device of the plurality of devices. The method further includes monitoring, for each device, capacity information and tasks performed during operation of the network. The method includes analyzing, based on the monitoring, each use of each device. The method also includes recommending, in response to the analyzing and by a learning model, a first upgrade to the network. The method further includes implementing the first upgrade.

An information processing method includes: obtaining, by a data analytics network element, terminal behavioral information of a plurality of terminals; determining, by the data analytics network element, network-side expected terminal behavioral information based on the terminal behavioral information; and sending, by the data analytics network element, the network-side expected terminal behavioral information to a user data management network element.

An information processing method includes: obtaining, by a data analytics network element, terminal behavioral information of a plurality of terminals; determining, by the data analytics network element, network-side expected terminal behavioral information based on the terminal behavioral information; and sending, by the data analytics network element, the network-side expected terminal behavioral information to a user data management network element.

Disclosed here is a system to automatically predict and resolve issues within a telecommunication network. Initially, the system builds a service registry to store dependence information within the network, which can include software components and hardware components. Various components of the network create logs of their operations. Machine learning models examine the logs and detect any issues. Upon detecting an issue or abnormal event, the system can automatically resolve the issue by determining the most similar issue occurring previously and determining a solution that resolved the previous most similar issue. In addition, the system can propagate the fix to dependent systems and/or notify the dependent systems of the issue.

A multi-tenant media processing platform system and method. At least a first media analysis service of a plurality of media analysis services is activated for at least a portion of an active communication session of an entity in the platform system. The first activated media analysis service performs a first media analysis on media of the active communication session that is collected by the platform system. The first activated media analysis service performs the first media analysis on the collected media while the communication session is active to generate a first media analysis result. During the active communication session, at least one media analysis result is applied.

The present disclosure generally discloses a problem event prediction capability. The problem event prediction capability may be configured to predict various types of problem events (e.g., customer problems, customer tickets, customer outages, network problems, network tickets, network outages, or the like, as well as various combinations thereof) based on various types of asynchronous machine data (e.g., alarms, alerts, triggers, machine logs, machine messages, diagnostic logs, diagnostic messages, or the like, as well as various combinations thereof). The problem event prediction capability may be configured to generate a set of problem prediction rules based on historical machine data and to apply the problem prediction rules to observed machine data in order to predict various types of problem events.

Systems, methods, and software for operational anomaly detection in communication systems is provided herein. An exemplary method includes obtaining a measured sequence of state information associated with the communications system during a first timeframe, processing the measured sequence of state information to determine a predicted sequence of state information for the communication system during a second timeframe, and monitoring current state information for the communication system over at least a portion of the second timeframe. The method also includes determining operational anomalies associated with the communication system based at least on a comparison between the current state information and the predicted sequence of state information.