In the present disclosure, a plurality of types of data are received from a base station and these pieces of data are learned, whereby a first model capable of calculating an estimated value of an index indicating an operation status of the base station is generated. Then this estimated value is compared to a measured value of the index indicating the operation status of the base station.

A system instantiates a unified interface for a telecommunications network including a Network Function Virtualization (NFV) architecture of an IP multimedia subsystem (IMS) with multiple Virtual Network Functions (VNFs) that each have multiple component VNFs. The system instantiates a tracing tool configured to evaluate performance of the IMS in response to execution of a test case, which includes a test script to test a computing product and where the performance of the IMS is based on packets captured concurrently from VNFs. The test script causes capture and analysis of parameters extracted from packets of the VNFs. In response to completing the test case, the system reports test results indicating the performance of the IMS relative to pass/fail criteria, where the test results are presented through the unified interface.

A method for remote monitoring includes (1) generating first sensor data from a first sensor at a first network node of a communications network and (2) sending the first sensor data from the first sensor to a second network node that is remote from the first network node, via the communications network. The first network node is powered from an electrical power grid that is separate from the communications network. The first sensor data may be raw sensor data and/or lossless sensor data.

A network administration device may include one or more processors to receive operational information regarding a plurality of network devices; receive flow information relating to at least one traffic flow; input the flow information to a model, where the model is generated based on a machine learning technique, and where the model is configured to identify predicted performance information of one or more network devices with regard to the at least one traffic flow based on the operational information; determine path information for the at least one traffic flow with regard to the one or more network devices based on the predicted performance information; and/or configure the one or more network devices to implement the path information for the traffic flow.

A failure effects estimating device includes an input unit that inputs a log and a traffic amount obtained from a communication system when an abnormality occurs, an estimating unit that estimates a failure effects amount in the communication system, on the basis of the log and the traffic amount, and an output unit that outputs the failure effects amount estimated by the estimating unit.

Embodiments of this application provide a network operation and maintenance method, to resolve a problem that a mainly manual network operation and maintenance method has high costs and low operation and maintenance efficiency. The method includes: A network intelligent unit obtains first network data, performs training based on the first network data to obtain a data model, and deploys the data model. The data model is a first model, a second model, or a third model. The first model is used to determine a network control instruction to be sent to an operation support system. The second model is used to determine a network control instruction to be sent to an element/network management system. The third model is used to determine a network control instruction to be sent to a network element.

Various embodiments include network computing devices and methods for computing resource management. A processor of a network computing device may determine a latency metric and a transaction volume metric for a network application, determine an autoscaling cost based on the determined latency metric and transaction volume metric, allocate to the network application computing resources based on the determined autoscaling cost, and provide the allocated computing resources to the network application.

Disclosed are systems and methods for a robust Self-Organizing Network (SON) framework that quantifies SON applications' control and management of a network into key performance indicators (KPI) that are leveraged to determine the impact of a SON's application effectiveness in regulating network parameters, which then dictates how the SON application operates. The disclosed framework is configured to receive multiple data streams from existing data sources, determine the performance of a node on a network, and then automatically perform SON operations based therefrom. The disclosed framework can utilize this information to predict additional and/or future opportunities for SON automation on the network.

An example method includes recording, by a node out of a plurality of nodes, occurrence of one or more baseline node events, generating a statistical data corresponding to a recorded occurrence of the one or more baseline node events over a pre-determined period, comparing one or more subsequent node events with the statistical data, and communicating data corresponding to the one or more subsequent node events to the central control device, in response to determining that the one or more subsequent node events satisfy the event deviation threshold.

For systems that can include, but are not limited to telecommunications, content distribution, and internetworking, the technologies described herein are generally directed to deploying network services, e.g., for large inventories of network equipment, complex deployments, and different processes being handled by isolated and difficult to modify legacy systems. For example, a method described herein can include receiving, by operations support equipment comprising a processor, a provisioning request for a network provisioning task. The method can further include, based on the network provisioning task, selecting, by the operations support equipment, a logical provisioning model to facilitate processing the provisioning request. Further, the network can include, based on the logical provisioning model, identifying, by the operations support equipment, an infrastructure engine to provision network equipment corresponding to the network provisioning task.