Patent classifications
H04L41/0863
Telecommunication network analytics platform
Methods, computer-readable media and devices are disclosed for selecting a plurality of network devices to perform a plurality of tasks in accordance with a set of functional network analytics instructions. For example, a processor deployed in a telecommunication network may receive a set of functional network analytics instructions compiled from a set of instructions in accordance with a functional network analytics platform application programming interface. The processor may further, in accordance with the set of functional network analytics instructions, select a plurality of network devices to perform a plurality of tasks, send the plurality of tasks to the plurality of network devices, receive control plane data from the plurality of network devices, correlate the control plane data in accordance with operations defined in the set of functional network analytics instructions to create resulting data, and forward the resulting data to at least one recipient device.
Systems and methods for efficient configuration file management and distribution by network management systems
A disclosed method may include (1) generating a configuration file that represents a specific configuration of a network device included in a network, (2) storing the configuration file that represents the specific configuration of the network device among a set of configuration files available via an NMS, (3) assigning to the configuration file via the NMS, a configuration identifier that uniquely identifies the configuration file among the set of configuration files available via the NMS, (4) receiving, via the NMS, a rollback request to restore the network device to the specific configuration based at least in part on the configuration identifier, and then in response to receiving the rollback request, (5) restoring the network device to the specific configuration based at least in part on the configuration file. Various other systems, methods, and computer-readable media are also disclosed.
DIFFERENCE BASED MULTIPLE DEVICE CONFIGURATION RENDERING AND EDITING
Example implementations relate to difference based configuration editing and rendering. A multi-editor can facilitate the implementation of a change to multiple configurations for multiple devices. Based on the change, the multi-editor can determine sets of operations for the multiple devices. After the sets of operations are applied to the multiple devices, comparisons can be made based on the multiple configurations before the change is implemented and after the change is implemented. Based on the comparisons, differences can be determined for the multiple configurations. Based on the differences, the multi-editor can render the multiple configurations with improved processing efficiency and rendering performance. Furthermore, the multi-editor can determine sets of counter operations based on the differences for implementing an undo command.
Migration of existing computing systems to cloud computing sites or virtual machines
Software, firmware, and systems are described herein that migrate functionality of a source physical computing device to a destination virtual machine. A non-production copy of data associated with a source physical computing device is created. A configuration of the source physical computing device is determined. A configuration for a destination virtual machine is determined based at least in part on the configuration of the source physical computing device. The destination virtual machine is provided access to data and metadata associated with the source physical computing device using the non-production copy of data associated with the source physical computing device.
MANAGING CONFIGURATIONS OF MOBILE DEVICES ACROSS MOBILITY CONFIGURATION ENVIRONMENTS
Embodiments described herein provide for systems and methods for managing configurations of mobile devices. A server may receive an instruction inputted via a graphical control to translate configurations from a first device environment to a second device environment. The server may identify, via an interface, a resource accessible by a first plurality of mobile devices in the first device environment based on the instruction. The server may determine, from the first device environment, a first profile identifying a first plurality of attributes defining a first configuration for the first plurality of mobile devices. The server may generate, using the first profile, a second profile identifying a second plurality of attributes defining a second configuration for a second plurality of mobile devices in the second device environment. The server may transmit, via the interface, the second profile to the second plurality of mobile devices.
Method and apparatus for restoring network device to factory defaults, and network device
Embodiments of the present disclosure disclose a method and an apparatus for restoring a NETCONF server to factory defaults, and relate to the field of configuration management technologies. A NETCONF server receives a command for restoring to factory defaults sent by a NETCONF client, where the command for restoring to factory defaults is a remote procedure call (RPC) command that is based on a Yang model of the NETCONF for restoring the NETCONF server to the factory defaults. The NETCONF server replaces data in the running configuration datastore with factory defaults of the NETCONF server according to the command for restoring to factory defaults.
Predicting the impact of network software upgrades on machine learning model performance
In one embodiment, a service receives software version data regarding versions of software executed by devices in a network. The service detects a version change in the version of software executed by one or more of the devices, based on the received software version data. The service makes a determination that a drop in data quality of input data for a machine learning model used to monitor the network is associated with the detected version change. The service reverts the one or more devices to a prior version of software, based on the determination that the drop in quality of the input data for the machine learning model used to monitor the network is associated with the detected version change.
Aborting network device upgrades
Examples of the present disclosure relate to updating network devices belonging to a group of network devices. In one aspect, a network controller coupled to the network devices of the group of network access devices, responsive to a first command, initiates a group update process for the network devices of the group is to update the network devices of the group sequentially according to an ordered list. Responsive to a second command during the group update process while a firmware image of a particular network device is updated, the network controller aborts the group update process for the network devices of the group. Aborting the group update process comprises removing a first subset of network devices subsequent to the particular network device in the ordered list from the ordered list such that the firmware image of the first subset of network devices will not be updated and rolling back the firmware image of the particular network device.
FILE-BASED CONFIGURATION OF ACCESS MANAGEMENT
Systems and methods include identification of a file defining a configuration associated with an access manager storing roles and profiles for accessing a plurality of cloud providers, determination that the file has changed and, in response to the determination, determine a current configuration of the access manager, determine a new configuration defined by the changed file, determine configuration changes based on the current configuration and the new configuration, and instruct the access manager to apply the configuration changes.
PREDICTING THE IMPACT OF NETWORK SOFTWARE UPGRADES ON MACHINE LEARNING MODEL PERFORMANCE
In one embodiment, a service receives software version data regarding versions of software executed by devices in a network. The service detects a version change in the version of software executed by one or more of the devices, based on the received software version data. The service makes a determination that a drop in data quality of input data for a machine learning model used to monitor the network is associated with the detected version change. The service reverts the one or more devices to a prior version of software, based on the determination that the drop in quality of the input data for the machine learning model used to monitor the network is associated with the detected version change.