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.

The present disclosure includes a method for receiving, by the processing system, reporting packets from one or more respective sensors of the plurality of sensors. Each reporting packet is sent from a respective sensor and indicates sensor data captured by the respective sensor; performing, by the processing system, one or more edge operations on one or more instances of sensor data received in the reporting packets. Generating one or more sensor kit packets based on the instances of sensor data. Each sensor kit packet includes at least one instance of sensor data. Outputting the sensor kit packets to the data handling platform. Receiving the sensor kit packets from the edge device. Generating the digital twin of said industrial setting including a digital replica of at least one industrial component of said industrial setting and being at least partially based on the sensor kit packets.

The present disclosure relates to system(s) and method(s) for predicting a Key Performance Indicator (KPI) in a telecommunication network is illustrated. The system is configured to monitor a set of counters and a Key Performance Indicator corresponding to a telecommunication network. The set of counters and the Key Performance Indicator (KPI) are monitored for a predefined time interval to gather sample data. The system is configured to analyze the sample data using a data analysis technique in order to identify a subset of counters, from the set of counters, influencing the KPI and a correlation coefficient associated with each counter from the subset of counters, wherein the correlation coefficient associated with each counter is identified after normalizing the subset of counters. The system is configured to apply regression on the subset of counters and the KPI in order to build a correlation equation between the subset of counters and the KPI.

The technology disclosed relates to building ensemble analytic rules for reusable operators and tuning an operations monitoring system. In particular, it relates to analyzing a metric stream by applying an ensemble analytical rule. After analysis of the metric stream by applying the ensemble analytical rule, quantized results are fed back for expert analysis. Then, one or more type I or type II errors are identified in the quantized results, and one or more of the parameters of the operators are automatically adjusted to correct the identified errors. The metric stream is further analyzed by applying the ensemble analytical rule with the automatically adjusted parameters.

In one embodiment, a network assurance service receives data regarding a monitored network. The service analyzes the received data using a machine learning-based model, to perform a network assurance function for the monitored network. The service detects a lowered performance of the machine learning-based model when a performance metric of the machine learning-based model is below a threshold for the performance metric. When it is determined that the lowered performance of the machine-learning based model is correlated with the sample rate of the received data, the service adjusts the sample rate of the data.

A method of converting a stream of samples at a first sampling rate to a stream of samples at a second sampling rate is disclosed, comprising: measuring the first sampling rate; determining a first upsampling factor from a basis comprising: the measured first sampling rate, the target value of the second sampling rate, and a resynchronisation error factor, the first upsampling factor being constrained to be an integer power of a predetermined integer value; and deriving, from a reference set of filter coefficients and from a ratio of the first upsampling factor to a reference upsampling factor, a first set of filter coefficients for use in a first interpolation filter, the reference set of filter coefficients being for a reference upsampling factor that is an integer power of the predetermined integer value.

Techniques and mechanisms are disclosed that enable a data collection system to adaptively control collection of data from one or more external data sources. At a high level, adaptively controlling collection of data from external data sources may include collecting performance information related to one or more data collection nodes and, in response to analyzing the collected performance information, adapting rates at which the data collection nodes send data collection requests to external data sources. Data collection performance information generally may include, but is not limited to, network traffic data, error messages generated by external data sources and/or data collection nodes, computing device performance information, and any other types of information related to a data collection node's ability to collect data from external data sources.

Disclosed is a variable-size sampling method under a data-streaming environment, including: calculating a maximum window size that satisfies a lower limitation of a predetermined uniformity confidence level at all times; inputting a data stream to be sampled; comparing a data stream length input until a current time point with the maximum window size; inspecting a sample size and a sampling fraction if the maximum window size is larger than the data stream length; performing sampling by generating a slot to increase the sample size if the current sample size is smaller than a predetermined percentage (P %) of the data stream; and directly performing sampling without generating a slot if the current sample size is equal to or larger than the predetermined percentage (P %) of the data stream. As a result, degradation of uniformity confidence during variable-size sampling under a real-time streaming environment can be prevented to improve sampling performance.

A method and an electronic apparatus for converting debugging information to a binary form and providing more information in the same capacity memory are disclosed. A control method of the electronic apparatus which records debugging information, the method includes: obtaining debugging information using a source code; adding index information corresponding to the debugging information to the debugging information and storing the debugging information in a buffer; and converting a plurality of pieces of index information stored in the buffer to a binary file.

Embodiments of the disclosure pertain to activating in-band OAM based on a triggering event. Aspects of the embodiments are directed to receiving a first notification indicating a problem in a network; triggering a data-collection feature on one or more nodes in the network for subsequent packets that traverse the one or more nodes; evaluating a subsequent packet that includes data augmented by the data collection feature; and determining the problem in the network based on the data augmented to the subsequent packet.