The packet capture manager uses a multi-tiered storage for storing captured network traffic. Captured packets are stored on a primary storage with a time-to-live according to a retention policy. The packet capture manager receives instructions from one or more network monitoring devices identifying one or more captured packets as packets of interest. The packet capture manager flags the identified packets as packets of interest, moves the flagged packets to a secondary storage, and changes the TTL of the moved packets. A machine learning model analyzes historical data of the instructions received from the one or more network monitoring devices. The packet capture manager uses the machine learning model to identify packets of interest and move identified packets to the secondary storage without specific instructions from a network monitoring device.

A signal analysis method comprising: receiving an input signal, the input signal comprising a symbol sequence; receiving samples of a reference signal based on a known sample rate, the reference signal comprising the same symbol sequence as the input signal; determining symbol points of the symbol sequence based on the samples; determining measurement times based on the symbol points; and determining at least one signal quality parameter at the measurement times, wherein the at least one signal quality parameter is indicative of a signal quality of the input signal. Further, a measurement system is described.

The packet capture manager uses a multi-tiered storage for storing captured network traffic. Captured packets are stored on a primary storage with a time-to-live according to a retention policy. The packet capture manager receives instructions from one or more network monitoring devices identifying one or more captured packets as packets of interest. The packet capture manager flags the identified packets as packets of interest, moves the flagged packets to a secondary storage, and changes the TTL of the moved packets. A machine learning model analyzes historical data of the instructions received from the one or more network monitoring devices. The packet capture manager uses the machine learning model to identify packets of interest and move identified packets to the secondary storage without specific instructions from a network monitoring device.

The packet capture manager uses a multi-tiered storage for storing captured network traffic. Captured packets are stored on a primary storage with a time-to-live according to a retention policy. The packet capture manager receives instructions from one or more network monitoring devices identifying one or more captured packets as packets of interest. The packet capture manager flags the identified packets as packets of interest, moves the flagged packets to a secondary storage, and changes the TTL of the moved packets. A machine learning model analyzes historical data of the instructions received from the one or more network monitoring devices. The packet capture manager uses the machine learning model to identify packets of interest and move identified packets to the secondary storage without specific instructions from a network monitoring device.

A communication method includes receiving a data packet and obtaining, according to a correspondence between a service package and a packet detection rule (PDR), a target service package corresponding to a target PDR matching the data packet. The method also includes performing data statistics collection based on the target service package, to obtain a data statistics collection result corresponding to the target service package. The method further includes sending the data statistics collection result corresponding to the target service package to a data analysis device. The method additionally includes receiving a data analysis result corresponding to the target service package. The method also includes performing traffic stimulation or traffic suppression on the target service package based on the data analysis result corresponding to the target service package.

A communication method includes receiving a data packet and obtaining, according to a correspondence between a service package and a packet detection rule (PDR), a target service package corresponding to a target PDR matching the data packet. The method also includes performing data statistics collection based on the target service package, to obtain a data statistics collection result corresponding to the target service package. The method further includes sending the data statistics collection result corresponding to the target service package to a data analysis device. The method additionally includes receiving a data analysis result corresponding to the target service package. The method also includes performing traffic stimulation or traffic suppression on the target service package based on the data analysis result corresponding to the target service package.

A technique may include receiving, from one or more sensors, sensor data samples; receiving radio network information data samples associated with a radio network; determining one or more associated sensor and radio network information data samples based on an association of one or more received sensor data samples with one or more of the received radio network information data samples; selecting at least some of the one or more associated sensor and radio network information data samples that are relevant to performance of the radio network; and forwarding the selected associated sensor and radio network information data samples for subsequent use.

Embodiments of the present invention are directed to facilitating performing online data decomposition. In accordance with aspects of the present disclosure, an incoming data point of a time series data set is obtained. Thereafter, an iterative process of estimating trend and seasonality is performed to decompose the incoming data point to a set of data components based on a particular set of previous data points of the time series data set and corresponding data components. Generally, the set of data components for the incoming data point include a trend component, a seasonality component, and a residual component. The set of data components is provided for analysis of the incoming data point, such as, for example, to identify data anomalies.

Embodiments of the present invention are directed to facilitating performing online data decomposition. In accordance with aspects of the present disclosure, an incoming data point of a time series data set is obtained. Thereafter, an iterative process of estimating trend and seasonality is performed to decompose the incoming data point to a set of data components based on a particular set of previous data points of the time series data set and corresponding data components. Generally, the set of data components for the incoming data point include a trend component, a seasonality component, and a residual component. The set of data components is provided for analysis of the incoming data point, such as, for example, to identify data anomalies.

Systems and methods for handling soft zoning violations comprise assigning a first target device and an endpoint device that is coupled to a switch port of a Fibre Channel (FC) switch to a zone(s). In embodiments, in response to the endpoint device logging into the FC switch, sampled traffic that originates at the endpoint device and ingresses at the switch port may be obtained. In response to determining that the sampled traffic comprises a second traffic that is intended for a second target device that has not been assigned to the zone(s), some action to restrict the second traffic may be performed such as to restrict the non-assigned traffic and prevent devices from sending potentially harmful traffic to other devices that are not assigned to a same zone.