A device receives network constraints associated with a network connected to a vehicle device, data collection constraints, and vehicle device constraints. The device determines a first sampling rate, a first time period, a second sampling rate, and a second time period for collecting vehicle data based on the network constraints, the data collection constraints, and the vehicle device constraints, wherein the first sampling rate is different than the second sampling rate. The device receives first vehicle data, provided at the first sampling rate and for the first time period, and second vehicle data, provided at the second sampling rate and for the second time period, and processes the first and second vehicle data, with a machine learning model, to identify a driving behavior or a vehicle issue. The device performs one or more actions based on the driving behavior or the vehicle issue.

In one embodiment, a node in a network detects an anomaly in the network based on a result of a machine learning-based anomaly detector analyzing network traffic. The node determines a packet capture policy for the anomaly by applying a machine learning-based classifier to the result of the anomaly detector. The node selects a set of packets from the analyzed traffic based on the packet capture policy. The node stores the selected set of packets for the detected anomaly.

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 method for adaptive data collection is proposed. The method may comprise detecting a network context for a communication node, and collecting network data for the communication node based at least in part on policy information associated with the network context. The policy information may describe a collection policy for the network data. According to an exemplary embodiment, the method may further comprise transmitting at least part of the collected network data and a tag derived from the policy information to a server for data composition.

Operations, Administration, and Maintenance (OAM) scaling systems and methods are implemented by a network function performed by one of a physical network element and a virtual network element executed on one or more processors. The OAM scaling method includes providing N packet services, N is an integer; and, responsive to determined OAM session scaling limits, providing OAM sessions for the N packet services in an oversubscribed manner, wherein the determined OAM session scaling limits include M OAM sessions supported by the network function, M is an integer and less than N.

There is provided a method for adaptive bitrate (ABR) adjustments in an IP network before making upshift of ABR level of media streams like video for live Over the Top (OTT) distribution. The invention is based on before upshifting of a current ABR level to a higher ABR level for one or more client devices, probing the network system with a higher bitrate of the data stream provided by e.g. replicating data in the data stream, and monitoring network conditions during probing. Based on the probing it is determined if the available resources in the network are sufficient to sustain an upshift of ABR-level for the client device.

A stream processing system in a first zone of a telecommunication network may obtain at least one policy for processing trace data of virtual network functions (VNFs) in the first zone, and obtain the trace data of the VNFs from a data distribution platform of the telecommunication network, where the trace data is published in accordance with a topic to the data distribution platform by the VNFs, and where the stream processing system comprises a subscriber to the topic. The first stream processing system may additionally forward at least a first portion of the trace data to a second stream processing system of the telecommunication network in accordance with the at least one policy, where the first portion comprises less than all of the trace data, and where the second stream processing system is for a region of the telecommunication network that includes the first zone and a second zone.

Systems, methods, and other embodiments associated with autonomous cloud-node scoping for big-data machine learning use cases are described. In some example embodiments, an automated scoping tool, method, and system are presented that, for each of multiple combinations of parameter values, (i) set a combination of parameter values describing a usage scenario, (ii) execute a machine learning application according to the combination of parameter values on a target cloud environment, and (iii) measure the computational cost for the execution of the machine learning application. A recommendation regarding configuration of central processing unit(s), graphics processing unit(s), and memory for the target cloud environment to execute the machine learning application is generated based on the measured computational costs.

A system and method for sampling application programming interface (API) execution traces in a computer system uses feature vectors of the API execution traces that are generated using trace-context information. The feature vectors are then used to group the API execution traces into clusters. For the cluster, sampling rates are generated so that a sampling rate is assigned to each of the clusters. The sampling rates are then applied to the API execution traces to adaptively sample the API execution traces based on the clusters to which the API execution traces belong.