Abstract: In one embodiment, a method comprises causing, by a network controller device, a first access point (AP) device to initiate a reverse sounding operation comprising wireles sly requesting a mobile constrained network device to transmit a null data packet (NDP) at a first transmission interval, wirelessly receiving the NDP at the first transmission interval, and generating a reception report describing reception of the NDP and including beamforming information; causing, by the network controller device, a second AP device to generate a corresponding reception report describing a corresponding wireless detection of the NDP at the first transmission interval; and causing, by the network controller device, the mobile constrained network device to connect to a selected one of the first AP device or the second AP device for an identified data flow based on the respective reception reports from the first and second AP devices.

Abstract: In one embodiment, a device clusters traffic characteristics of traffic associated with a plurality of online applications into one or more clusters. The device determines representative traffic characteristics for a particular cluster in the one or more clusters. The device generates, based on the representative traffic characteristics, a probing strategy for the plurality of online applications associated with the particular cluster. The device causes path probes to be sent along one or more network paths in accordance with the probing strategy.

Abstract: In one embodiment, a device forms a plurality of clusters of network paths used to convey traffic for an online application by applying clustering to telemetry data for those network paths. The device determines a predictability metric for a particular cluster in the plurality of clusters. The device provides an indication of the predictability metric for the particular cluster for display. The device enables, based in part on the predictability metric, predictive routing for the network paths in the particular cluster.

Abstract: In one embodiment, a supervisory device in a network notifies, via an access point of the network, a node as to an ability of the network to support virtual access points. The supervisory device receives, in response to notifying the node, information from the node regarding characteristics of the node. The supervisory device selects, based on the characteristics of the node, a plurality of access points in the network to form a virtual access point with which the node may communicate. The supervisory device configures the plurality of access points to function as the virtual access point, wherein the node communicates with the network via the virtual access point.

Abstract: In one embodiment, a device obtains telemetry data for a path in a network that is used to convey traffic associated with an online application. The device identifies, based on the telemetry data, oscillations of the path between a first state in which the path provides acceptable user experience for the online application and a second state in which the path does not provide acceptable user experience for the online application. The device determines a stability metric that quantifies the oscillations of the path. The device provides an indication of the oscillations of the path, based in part on the stability metric.

Abstract: In one embodiment, a device identifies a plurality of paths between a pair of network addresses, wherein one of the pair of network addresses is associated with an online application. The device obtains telemetry data from the plurality of paths for the online application. The device computes, based on the telemetry data, fate sharing metrics for the plurality of paths. The device controls routing of application traffic between the pair of network addresses, based on the fate sharing metrics for the plurality of paths.

Abstract: In one embodiment, a device receives path telemetry data for one or more network paths in a network over which traffic for an online application is conveyed. The device computes time series dynamics for the path telemetry data. The device determines a mapping of the time series dynamics to application experience metrics for the online application. The device routes traffic associated with the online application based on the mapping.

Abstract: In one embodiment, a device makes a prediction regarding service level agreement violations by a network transport available between a site and an online application. The device associates a risk metric with the prediction, based in part on a type of the network transport. The device computes a longevity metric for the prediction that indicates an expected validity period for the prediction. The device cause traffic to be routed between the site and the online application using the network transport, based on the prediction and its associated risk metric and its longevity metric.

Abstract: In one embodiment, a device computes an efficiency metric regarding ingestion of telemetry data from a particular portion of a network by a predictive routing engine used to make predictive routing decisions for that portion of the network. The device makes a comparison between the efficiency metric and one or more control rules. The device determines, based on the comparison, whether ingestion of the telemetry data from the particular portion of the network by the predictive routing engine should be disabled. The device causes the predictive routing engine to stop ingesting telemetry data from the particular portion of the network.

Abstract: In one embodiment, a device computes states of a network path associated with an online application by representing time series of telemetry data regarding the network path as discrete values. The device makes, using a machine learning model, a prediction that a quality of experience metric for the online application will be degraded, based on a particular transition pattern of the states being observed for the network path. The device determines one or more performance metrics for the machine learning model with respect to the network path. The device provides an indication of the particular transition pattern of the states for display, based in part on the one or more performance metrics for the machine learning model with respect to the network path.

Abstract: In one embodiment, a device obtains data indicative of quality of experience for an online application. The device predicts, based on the data, path performances of network paths between an endpoint and the online application for different traffic loads. The device selects traffic loads for the network paths between the endpoint and the online application, based on the path performances predicted by the device. The device causes application traffic to be load balanced across the network paths between the endpoint and the online application, in accordance with those traffic loads selected by the device.

Abstract: In one embodiment, a device obtains path probe data between one or more end-user sites and an online application. The device makes, based on the path probe data, a prediction as to whether a direct Internet access path or a backhaul path would offer better application performance for the online application. The device generates, based on the prediction, a split tunnel policy for a particular end-user site. The device causes a particular end-user site to connect to the online application in accordance with the split tunnel policy.

Abstract: In one embodiment, a device obtains metadata for routing decisions made by a predictive routing service for a plurality of network deployments. The device identifies a network topology for a network deployment that does not use the predictive routing service. The device estimates, based on the metadata for routing decisions made by the predictive routing service, performance metrics for the predictive routing service were it to be used to make routing decisions for the network topology. The device sends, to a user interface, report data indicative of the performance metrics estimated for the predictive routing service were it to be used to make routing decisions for the network topology.

Abstract: In one embodiment, a service receives software version data regarding versions of software executed by devices in a network. The service detects a version change in the version of software executed by one or more of the devices, based on the received software version data. The service makes a determination that a drop in data quality of input data for a machine learning model used to monitor the network is associated with the detected version change. The service reverts the one or more devices to a prior version of software, based on the determination that the drop in quality of the input data for the machine learning model used to monitor the network is associated with the detected version change.

Abstract: In one embodiment, a device obtains performance data regarding failures of a tunnel in a network. The device generates a failure profile for the tunnel by applying machine learning to the performance data regarding the failures of the tunnel. The device determines, based on the failure profile for the tunnel, whether the tunnel exhibits failure flapping behavior. The device adjusts one or more Bidirectional Forwarding Detection (BFD) probing timers used to detect failures of the tunnel, based on the determination as to whether the tunnel exhibits failure flapping behavior.

Abstract: In one embodiment, a network assurance service that monitors a network maps time series of values of key performance indicator (KPIs) measured from the network to lists of unique values from the time series. The service sets a target alarm rate for anomaly detection alarms raised by the network assurance service. The service uses an optimization function to identify a set of thresholds for the KPIs. The optimization function is based on: a comparison between the target alarm rate and a fraction of network issues flagged by the service as outliers, KPI thresholds selected based on the lists of unique values from the time series, and a number of thresholds that the KPIs must cross for the service to raise an alarm. The service raises an anomaly detection alarm for the monitored network based on the identified set of thresholds for the KPIs.

Abstract: In one embodiment, a supervisory device in a network forms a virtual access point (VAP) for a node in the network. A set of access points (APs) in the network are mapped to the VAP as part of a VAP mapping and the node treats the APs in the VAP mapping as a single AP for purposes of communicating with the network. The supervisory device receives measurements from the APs in the VAP mapping regarding communications associated with the node. The supervisory device identifies a movement of the node based on the received measurements from the APs in the VAP mapping. The supervisory device adjusts the set of APs in the VAP mapping based on the identified movement of the node.

Abstract: In one embodiment, a method comprises: first determining, by a controller, a target load for each of at least first and second wireless data networks rooted by respective first and second root network devices; second determining, by the controller, that at least a prescribed minimum number of network devices attached within the first wireless data network need to migrate from the first wireless data network to the second wireless data network based on the respective target loads; and causing, by the controller, the prescribed minimum number of network devices to migrate from the first wireless data network to the second wireless data network, based on the controller sending to the first and second root network devices enrollment priorities to be advertised by the first and second root network devices, respectively.

Abstract: In one embodiment, a label stability analyzer service receives classification data indicative of device type labels assigned to endpoints in a network by a device classification service. The label stability analyzer service counts device type label changes made by the device classification service to the endpoints. The label stability analyzer service computes variability metrics for the device type labels, wherein the variability metric for a device type label is based on a count of the device type label changes associated with that label. The label stability analyzer service determines, based on one of the variability metrics for a particular one of the device type labels exceeding a threshold value, a configuration change for the device classification service that adjusts how the device classification service applies the particular label to endpoints. The label stability analyzer service provides the configuration change to the device classification service.

Abstract: In one embodiment, techniques are shown and described relating to traffic-based inference of influence domains in a network by using learning machines. In particular, in one embodiment, a management device computes a time-based traffic matrix indicating traffic between pairs of transmitter and receiver nodes in a computer network, and also determines a time-based quality parameter for a particular node in the computer network. By correlating the time-based traffic matrix and time-based quality parameter for the particular node, the device may then determine an influence of particular traffic of the traffic matrix on the particular node.