A monitoring system using agents to dynamically collect state information at controllable intensity levels from components of systems. The system receives state information collected by an agent at a particular intensity level, and processes the state information to determine an updated intensity level for collecting state information by the agent, or by some other agent. The state information may include data indicating the performance of one or more components, such as process response times or other metrics. The intensity level for collecting further state information can be increased when, among other things, previously collected state information indicates more detailed monitoring for the component is appropriate. The intensity level for collecting further state information can be decreased when, among other things, previously collected state information indicates continued expected behavior.

Data reduction techniques are provided for a multi-sensor IoT environment. An exemplary method comprises: dynamically determining, by a device within a distributed network comprised of a plurality of sensors, an amount of sensor data to be collected by and/or transmitted by a sensor within the distributed network based on at least one predefined spatial-based rule and/or at least one predefined temporal-based rule; and processing the sensor data based on the dynamically determined amount of sensor data. A percentage of the plurality of sensors within the distributed network that collect and/or transmit the sensor data can optionally be specified. One or more sensors optionally collect the sensor data at a default resolution and a predefined spatial-based rule and/or a predefined temporal-based rule specifies a predefined trigger for at least one sensor to collect and/or transmit the sensor data at a higher resolution.

A network device that operates as an analysis platform for analysis of event data records that can provide a flexible approach to event data record aggregation. For example, aggregation can be flexibly turned on or off and dynamically adjusted based on event record volume and other factors such as network capacity or throughput. Devices that are instructed to aggregate records can also be instructed to archive the raw records, e.g., to maintain a full fidelity log of events. Devices can further be instructed to utilize a mixed queue approach to determine an order to deliver those records that includes both older records and newer records.

Methods, systems, and computer program products to modulate telemetry data as a function to represent the performance of a network and/or individual devices connected to the network. In embodiments, the method includes receiving telemetry data that has been sampled at a given point of time, wherein the telemetry data is associated with a performance metric of a device; processing the telemetry data as a function representing performance of the network device, wherein processing the telemetry data comprises modulating the telemetry data at the given point of time to previously sampled telemetry data based on the function; and demodulating the modulated telemetry data. In embodiments, the method also includes transferring the modulated telemetry data for reporting.

This disclosure describes techniques and mechanisms for intelligently sampling packet flows within a network. The techniques enable the sampling of a limited set of packet flows that show greatest amount of information about the network from the packet flows in order to provide the greatest insight on application performance, network packet, and critical events within the network. Additionally, the techniques provide configurable parameters, such that the techniques are customizable for each user's network.

An example network device includes memory, a communication unit, and processing circuitry coupled to the memory and the communication unit. The processing circuitry is configured to receive first samples of flows from an interface of another network device sampled at a first sampling rate and determine a first parameter based on the first samples. The processing circuitry is configured to receive second samples of flows from the interface sampled at a second sampling rate, wherein the second sampling rate is different than the first sampling rate and determine a second parameter based on the second samples. The processing circuitry is configured to determine a third sampling rate based on the first parameter and the second parameter, control the communication unit to transmit a signal indicative of the third sampling rate to the another network device; and receive third samples of flows from the interface sampled at the third sampling rate.

Systems and methods that adaptively model network traffic to predict network capacity utilization and quality of experience into the future. The adaptive model of network traffic may be used to recommend capacity upgrades based on a score expressed in a QoE space.

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.

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.

Systems and methods are provided for dynamic metering adjustment for service management of a computing platform. For example, a plurality of virtual machines are provisioned across a plurality of computing nodes of a computing platform. Data samples are collected for a metric that is monitored with regard to resource utilization in the computing platform by the virtual machines. The data samples are initially collected at a predefined sampling frequency. The data samples collected over time for the metric are analyzed to determine an amount of deviation in values of the collected data samples. A new sampling frequency is determined for collecting data samples for the metric based on the determined amount of deviation. The new sampling frequency is applied to collect data samples for the metric, wherein the new sampling frequency is less than the predefined sampling frequency.