In this disclosure, in a network comprising a plurality of network devices, a network device includes processing circuitry configured to: receive packet data corresponding to a network flow originating at a first device, the packet data destined to a second device; generate an entropy label to add to a label stack of the packet data, wherein the entropy label is generated from one or more attributes corresponding to the network flow that originated at the first device and is destined to the second device; generate a flow record including the entropy label, wherein the entropy label identifies the network flow amongst a plurality of network flows in the network; and send, to a controller of the network, the flow record, wherein the controller identifies the flow record based on the entropy label corresponding to the network flow originating at the first device and is destined to the second device.

A processing system may obtain a first sampled flow record for a first flow in a network, comprising information regarding selected packets of the first flow, derive, from the first sampled flow record, a data volume and a duration of the first flow, and determine a first flow metric for the first flow that is calculated from the data volume and the duration, where the first flow metric is one of a plurality of flow metrics for a plurality of flows, and where the plurality of flow metrics is determined from the plurality of sampled flow records associated with the plurality of flows. The processing system may then classify the first flow into one of at least two classes, based upon the first flow metric and at least a first flow metric threshold.

A processing system may obtain a first sampled flow record for a first flow in a network, comprising information regarding selected packets of the first flow, derive, from the first sampled flow record, a data volume and a duration of the first flow, and determine a first flow metric for the first flow that is calculated from the data volume and the duration, where the first flow metric is one of a plurality of flow metrics for a plurality of flows, and where the plurality of flow metrics is determined from the plurality of sampled flow records associated with the plurality of flows. The processing system may then classify the first flow into one of at least two classes, based upon the first flow metric and at least a first flow metric threshold.

Monitoring an operational characteristic of a data communication device within a network includes sampling an operational characteristic of the data communication device at a fine-grain sample rate over a first sampling interval to produce fine-grain samples of the operational characteristic of the data communication device, training a machine learning algorithm using the fine-grain samples of the operational characteristic of the data communication device, the fine-grain sample rate, and a coarse-grain sample rate that is less than the fine-grain sample rate, sampling the operational characteristic of the data communication device at the coarse-grain sample rate over a second sampling interval to produce coarse-grain samples of the operational characteristic of the data communication device, and using the machine learning algorithm to process the coarse-grain samples of the operational characteristic of the data communication device to produce accuracy-enhanced samples of the operational characteristic of the data communication device.

Monitoring an operational characteristic of a data communication device within a network includes sampling an operational characteristic of the data communication device at a fine-grain sample rate over a first sampling interval to produce fine-grain samples of the operational characteristic of the data communication device, training a machine learning algorithm using the fine-grain samples of the operational characteristic of the data communication device, the fine-grain sample rate, and a coarse-grain sample rate that is less than the fine-grain sample rate, sampling the operational characteristic of the data communication device at the coarse-grain sample rate over a second sampling interval to produce coarse-grain samples of the operational characteristic of the data communication device, and using the machine learning algorithm to process the coarse-grain samples of the operational characteristic of the data communication device to produce accuracy-enhanced samples of the operational characteristic of the data communication device.

A device may receive information associated with a service chain to be implemented in association with a flow. The information associated with the service chain may include a source network address associated with the flow, a destination network address associated with the flow, a set of protocols associated with the flow, and a set of network services, of the service chain, to be implemented in association with the flow. The device may implement the service chain in association with the flow. The device may receive network traffic information associated with the flow based on implementing the service chain in association with the flow. The device may modify the service chain based on the network traffic information associated with the flow to permit a modified service chain to be implemented in association with the flow.

A device may receive information associated with a service chain to be implemented in association with a flow. The information associated with the service chain may include a source network address associated with the flow, a destination network address associated with the flow, a set of protocols associated with the flow, and a set of network services, of the service chain, to be implemented in association with the flow. The device may implement the service chain in association with the flow. The device may receive network traffic information associated with the flow based on implementing the service chain in association with the flow. The device may modify the service chain based on the network traffic information associated with the flow to permit a modified service chain to be implemented in association with the flow.

A sampling frequency recommendation method, apparatus, and device, and a storage medium relating to the field of communications technologies are disclosed. The sampling frequency recommendation method includes: obtaining a network key performance indicator of a to-be-analyzed data stream; sampling the network key performance indicator based on a plurality of different sampling frequencies to obtain an experience quality sequence corresponding to each sampling frequency, where the plurality of different sampling frequencies include one standard sampling frequency and at least two to-be-tested sampling frequencies, and the standard sampling frequency is greater than each to-be-tested sampling frequency; and determining a matching degree between an experience quality sequence corresponding to each to-be-tested sampling frequency and a standard experience quality sequence, and determining a recommended sampling frequency based on the matching degree between the experience quality sequence corresponding to each to-be-tested sampling frequency and the standard experience quality sequence.

A sampling frequency recommendation method, apparatus, and device, and a storage medium relating to the field of communications technologies are disclosed. The sampling frequency recommendation method includes: obtaining a network key performance indicator of a to-be-analyzed data stream; sampling the network key performance indicator based on a plurality of different sampling frequencies to obtain an experience quality sequence corresponding to each sampling frequency, where the plurality of different sampling frequencies include one standard sampling frequency and at least two to-be-tested sampling frequencies, and the standard sampling frequency is greater than each to-be-tested sampling frequency; and determining a matching degree between an experience quality sequence corresponding to each to-be-tested sampling frequency and a standard experience quality sequence, and determining a recommended sampling frequency based on the matching degree between the experience quality sequence corresponding to each to-be-tested sampling frequency and the standard experience quality sequence.

The dynamically generation and evaluation of User Activity Records (UARs) is presented herein to determine which UARs to forward for analytics processing, and how much information to include with the forwarded UARs. To that end, UARs are identified as normal, e.g., those UARs satisfy an evaluation condition, e.g., a threshold condition, and or as abnormal, e.g., those UARs that do not satisfy an evaluation condition, e.g., the threshold condition. For those UARS identified as normal, only a small subset of the normal UARs are forwarded for further analysis to reduce the data volume associated with these normal UARs. For those UARs identified as abnormal, enrichment data is appended to the generated UAR to generate a detailed UAR, all which is forwarded for further analysis.