A method includes partitioning a set of configuration management (CM) data for one or more cellular network devices into multiple distinct time intervals, each time interval associated with a distinct set of CM settings at the one or more cellular network devices, the CM data comprising multiple CM parameters. The method also includes determining a regression model based on the set of CM data. The method also includes applying the regression model to compute a distinct set of scores and compare the set of scores to estimate whether a performance of the one or more cellular network devices has changed during a second time interval relative to a first time interval.

A method includes partitioning a set of configuration management (CM) data for one or more cellular network devices into multiple distinct time intervals, each time interval associated with a distinct set of CM settings at the one or more cellular network devices, the CM data comprising multiple CM parameters. The method also includes determining a regression model based on the set of CM data. The method also includes applying the regression model to compute a distinct set of scores and compare the set of scores to estimate whether a performance of the one or more cellular network devices has changed during a second time interval relative to a first time interval.

The disclosed technology includes a health engine that monitors and modifies customer-premises equipment (CPE) devices. The health engine can detect patterns in CPE device behavior, identify problems with CPE devices, and adjust CPE device configurations proactively or reactively to address problems or prevent problems. In some implementations, the health engine can instruct a CPE device or gateway to restart, update its software or firmware, notify a user of the CPE device of an unhealthy behavior pattern in a CPE device. The health engine can modify a CPE device prior to a user using the device or when the CPE device is inactive.

A computer network optimization methodology is disclosed. In a computer-implemented method, components of a computing environment are automatically monitored, and have a feature selection analysis performed thereon. Provided the feature selection analysis determines that features of the components are in frequent communication and generating network latency. Provided the feature selection analysis determines that features of the components are not well defined, a similarity analysis of the features is performed. Results of the feature selection methodology are generated, and the components involved in the network traffic latency are reassigned to migrate the latency.

A computer network optimization methodology is disclosed. In a computer-implemented method, components of a computing environment are automatically monitored, and have a feature selection analysis performed thereon. Provided the feature selection analysis determines that features of the components are in frequent communication and generating network latency. Provided the feature selection analysis determines that features of the components are not well defined, a similarity analysis of the features is performed. Results of the feature selection methodology are generated, and the components involved in the network traffic latency are reassigned to migrate the latency.

A method by a wireless device is provided for optimized reconfiguration of radio link monitoring (RLM) and beam monitoring. The method includes receiving, from a first network node, a first message comprising at least one RLM parameter. A second message indicating activation of the at least one RLM parameter associated with the first message is received. The second message is a lower layer signal compared to the first message.

A method by a wireless device is provided for optimized reconfiguration of radio link monitoring (RLM) and beam monitoring. The method includes receiving, from a first network node, a first message comprising at least one RLM parameter. A second message indicating activation of the at least one RLM parameter associated with the first message is received. The second message is a lower layer signal compared to the first message.

Embodiments of this present disclosure may include a system that includes a first network device. The first network device may perform an operation according to a device configuration file. The system may also include a second network device that directly communicatively couples to the first network device through a peer-to-peer (P-P) communication network. The second network device may include a backup file of the device configuration file. The second network device may transmit the backup file of the device configuration file to the first network device in response to detecting that the first network device is lacking the device configuration file.

Embodiments of this present disclosure may include a system that includes a first network device. The first network device may perform an operation according to a device configuration file. The system may also include a second network device that directly communicatively couples to the first network device through a peer-to-peer (P-P) communication network. The second network device may include a backup file of the device configuration file. The second network device may transmit the backup file of the device configuration file to the first network device in response to detecting that the first network device is lacking the device configuration file.

A computer-readable medium contains cybersecurity configuration settings (CCS) generating file(s) including instructions when executed cause a processor of a computer located at a node in a networked system having computers including at least one computer system class to generate CCS. The CCS generating file includes group policy objects (GPOs) applicable to all computers, policy setting scripts that are applicable to <all the computer s, and group policy definition files which provide a policy setting library for the computer class. Execution of the CCS generating file at the node automatically generates the CCS for cybersecurity protection of the node. The computer class can include computer classes that include ≥2 different operating systems, and there can be a CCS generating file for each computer class. The CCS generating file can be a single multi-class CCS generating file that includes a plurality of CCS generating files.