Abstract: In one embodiment, a device classification service receives telemetry data indicative of behavioral characteristics of a plurality of devices in a network. The service obtains side information for the telemetry data. The service applies metric learning to the telemetry data and side information, to construct a distance function. The service uses the distance function to cluster the telemetry data into device clusters. The service associates a device type label with a particular device cluster.

Abstract: In one embodiment, a supervisory service for a software-defined wide area network (SD-WAN) uses a plurality of different decision thresholds for a machine learning-based classifier, to predict tunnel failures of a tunnel in the SD-WAN. The supervisory service captures performance data indicative of performance of the classifier when using the different decision thresholds. The supervisory service selects, based on the captured performance data, a particular decision threshold for the classifier, in an attempt to optimize the performance of the classifier. The supervisory service uses the selected decision threshold for the classifier, to predict a tunnel failure of the tunnel.

Abstract: In one embodiment, a device predicts a failure of a first tunnel in a software-defined wide area network (SD-WAN). The device makes a prediction as to whether a second tunnel in the SD-WAN will satisfy a service level agreement (SLA) associated with traffic on the first tunnel. The device proactively reroutes the traffic from the first tunnel onto the second tunnel, based on the prediction as to whether that the second tunnel will satisfy the SLA of the traffic. The device monitors one or more quality of service (QoS) metrics for the rerouted traffic, to ensure that the second tunnel satisfies the SLA of the traffic.

Abstract: In one embodiment, a device classification service receives telemetry data indicative of behavioral characteristics of a plurality of devices in a network. The service obtains side information for the telemetry data. The service applies metric learning to the telemetry data and side information, to construct a distance function. The service uses the distance function to cluster the telemetry data into device clusters. The service associates a device type label with a particular device cluster.

Abstract: In one embodiment, a device predicts a failure of a first tunnel in a software-defined wide area network (SD-WAN). The device makes a prediction as to whether a second tunnel in the SD-WAN will satisfy a service level agreement (SLA) associated with traffic on the first tunnel. The device proactively reroutes the traffic from the first tunnel onto the second tunnel, based on the prediction as to whether that the second tunnel will satisfy the SLA of the traffic. The device monitors one or more quality of service (QoS) metrics for the rerouted traffic, to ensure that the second tunnel satisfies the SLA of the traffic.

Abstract: In one embodiment, a supervisory service for a software-defined wide area network (SD-WAN) uses a plurality of different decision thresholds for a machine learning-based classifier, to predict tunnel failures of a tunnel in the SD-WAN. The supervisory service captures performance data indicative of performance of the classifier when using the different decision thresholds. The supervisory service selects, based on the captured performance data, a particular decision threshold for the classifier, in an attempt to optimize the performance of the classifier. The supervisory service uses the selected decision threshold for the classifier, to predict a tunnel failure of the tunnel.

Abstract: In one embodiment, a network assurance service receives a first set of telemetry data captured in a first network monitored by the network assurance service. The network assurance service computes, for each of a plurality of other networks monitored by the service, a similarity score between the first set of telemetry data and a set of telemetry data captured in that other network. The service selects a machine learning-based anomaly detector trained using a particular one of the sets of telemetry data captured in one of the plurality of other networks, based on the computed similarity score between the first set of telemetry data and the particular set of telemetry data captured in one of the plurality of other networks. The service uses the selected anomaly detector to assess telemetry data from the first network, until the service has received a threshold amount of telemetry data for the first network.

Abstract: In one embodiment, a network assurance service applies labels to feature vectors of network characteristics associated with a plurality of wireless access points in the network. An applied label for a feature vector indicates whether the access point associated with the feature vector experienced a threshold number of onboarding delays within a given time window. The service, based on the feature vectors and labels, trains a plurality of machine learning-based classifiers to predict onboarding delays, and uses one or more of the trained plurality of classifiers to predict onboarding delays for a particular access point. The service calculates one or more classifier performance metrics for the one or more classifiers based on the predicted onboarding delays for the particular access point. The service selects a particular one of the classifiers to monitor the network characteristics associated with the particular access point, based on the one or more classifier performance metrics.

Abstract: In one embodiment, a network assurance service receives a first set of telemetry data captured in a first network monitored by the network assurance service. The network assurance service computes, for each of a plurality of other networks monitored by the service, a similarity score between the first set of telemetry data and a set of telemetry data captured in that other network. The service selects a machine learning-based anomaly detector trained using a particular one of the sets of telemetry data captured in one of the plurality of other networks, based on the computed similarity score between the first set of telemetry data and the particular set of telemetry data captured in one of the plurality of other networks. The service uses the selected anomaly detector to assess telemetry data from the first network, until the service has received a threshold amount of telemetry data for the first network.

Abstract: In one embodiment, a network assurance service applies labels to feature vectors of network characteristics associated with a plurality of wireless access points in the network. An applied label for a feature vector indicates whether the access point associated with the feature vector experienced a threshold number of onboarding delays within a given time window. The service, based on the feature vectors and labels, trains a plurality of machine learning-based classifiers to predict onboarding delays, and uses one or more of the trained plurality of classifiers to predict onboarding delays for a particular access point. The service calculates one or more classifier performance metrics for the one or more classifiers based on the predicted onboarding delays for the particular access point. The service selects a particular one of the classifiers to monitor the network characteristics associated with the particular access point, based on the one or more classifier performance metrics.