A network management apparatus according to an embodiment includes a storage device configured to store information indicating connection relationships between a plurality of communication devices and communication paths in a network configuration and rank information indicating ranks of the plurality of communication devices in the network configuration and a determination unit configured to, when a fault has occurred in a communication device, determine that a communication device influenced by the fault is within a fault influence range based on the information stored in the storage device.

A network management apparatus according to an embodiment includes a storage device configured to store information indicating connection relationships between a plurality of communication devices and communication paths in a network configuration and rank information indicating ranks of the plurality of communication devices in the network configuration and a determination unit configured to, when a fault has occurred in a communication device, determine that a communication device influenced by the fault is within a fault influence range based on the information stored in the storage device.

This disclosure provides a measurement method and an acquisition method for a packet loss rate, a terminal, and a network device. The measurement method for a packet loss rate is applicable to a terminal, and comprises: acquiring configuration information concerning an uplink packet loss rate measurement, the configuration information comprising: measurement time window information, measured service information and condition information for triggering uplink packet loss rate measurement reporting; measuring, according to the configuration information, an uplink packet loss rate; and sending report information to a network device according to the uplink packet loss rate.

This disclosure provides a measurement method and an acquisition method for a packet loss rate, a terminal, and a network device. The measurement method for a packet loss rate is applicable to a terminal, and comprises: acquiring configuration information concerning an uplink packet loss rate measurement, the configuration information comprising: measurement time window information, measured service information and condition information for triggering uplink packet loss rate measurement reporting; measuring, according to the configuration information, an uplink packet loss rate; and sending report information to a network device according to the uplink packet loss rate.

Methods and apparatus for obtaining status from an isolated AP that cannot connect to a remote management server are described. The status information is obtained from a second device and then provided, via the second device, to the remote management server. At least some of the disclosed embodiments are utilized in a system including a plurality of access points, which can provide alternate pathways to the remote management server. The remote management server determines a remedial action based on the status information.

Methods and apparatus for obtaining status from an isolated AP that cannot connect to a remote management server are described. The status information is obtained from a second device and then provided, via the second device, to the remote management server. At least some of the disclosed embodiments are utilized in a system including a plurality of access points, which can provide alternate pathways to the remote management server. The remote management server determines a remedial action based on the status information.

A device includes a processor and a memory. The processor effectuates operations including monitoring enterprise network traffic associated with one or more user device (UE). The processor further effectuates operations including comparing the enterprise network traffic to a UE profile associated with each of the one or more UE. The processor further effectuates operations including determining whether the comparison indicates that a predetermined threshold has been exceeded. The processor further effectuates operations including in response to the indication that the predetermined threshold has been exceeded, generating an alert, wherein exceeding the predetermined threshold is indicative of a denial of service attack on an enterprise network or an attempt to remove enterprise data via the one or more UE.

A device includes a processor and a memory. The processor effectuates operations including monitoring enterprise network traffic associated with one or more user device (UE). The processor further effectuates operations including comparing the enterprise network traffic to a UE profile associated with each of the one or more UE. The processor further effectuates operations including determining whether the comparison indicates that a predetermined threshold has been exceeded. The processor further effectuates operations including in response to the indication that the predetermined threshold has been exceeded, generating an alert, wherein exceeding the predetermined threshold is indicative of a denial of service attack on an enterprise network or an attempt to remove enterprise data via the one or more UE.

A system, method, and computer-readable medium are disclosed for performing a data center monitoring and management operation. The data center monitoring and management operation includes: monitoring data center assets within a data center; identifying an issue within the data center, the issue being associated with an operational situation associated with a particular component of the data center; associating the issue with a particular point in time; and, informing a user about the issue, the informing including information regarding the particular point in time, the informing including a graphical depiction of the particular component of the data center and the issue within the data center.

In an embodiment computer-readable training data records characterizing operation of a communication network may be used to train a machine learning (ML) model of network performance to predict expected performance characteristics, each training data record including operational features and observed performance characteristics of the communication network. The ML model may compute mappings of feature-values pairs to predicted performance characteristics. For a first subset of training data records, a fair distribution of first quantitative contributions of operational features of predicted performance characteristics may be computed, the first subset representing a baseline of observed performance characteristics. For a second subset of training data records, a fair distribution of second respective quantitative contributions of predicted performance characteristics may be computed, the second subset representing a problematic performance characteristic. Comparing the first and second quantitative contributions may determine a degradation metric for associating operational features of the second subset with the problematic performance characteristic.