Abstract: In various embodiments, a device obtains a set of device classification rules. Each device classification rule specifies one or more attributes from a set of attributes and being configured to assign a device type to an endpoint in a network when the endpoint exhibits the one or more attributes specified by that rule. The device forms a graphical representation of the set of attributes. The device performs an analysis of the graphical representation of the set of attributes. The device provides a result of the analysis to a user interface.

Abstract: In one embodiment, a device obtains a first set of measurements of a path metric for a path in a network that are measured using periodic probing of the path. The device obtains a second set of measurements of the path metric for the path that are measured using fine-grained probing of the path at a higher frequency than that of the periodic probing. The device generates a predictive model that predicts values of the path metric, based on the first set of measurements and on the second set of measurements. The device causes, based on a value of the path metric predicted by the predictive model, traffic to be rerouted from the path to another path in the network.

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 network assurance service that monitors a network detects an anomaly in the network by applying an anomaly detector to telemetry data collected from the network. The service sends first data to a user interface that causes the interface to present the detected anomaly and one or more candidate root cause metrics from the telemetry data associated with the detected anomaly. The service receives feedback regarding the candidate root cause metric(s) and learns a root cause of the anomaly as one or more thresholds of the candidate root cause metric(s), based in part on the received feedback regarding the candidate root cause metric(s). The service sends second data to the user interface that causes the user interface to present at least one of the candidate root cause metric(s) as a candidate root cause of a subsequent detected anomaly, based on the learned threshold(s).

Abstract: In various embodiments, a device classification service obtains device telemetry data indicative of declarative attributes of a device in a network and indicative of behavioral attributes of that device. The device classification service labels the device with a device type, based on the device telemetry data. The device classification service detects device type spoofing exhibited by the device using a model that models a relationship between the declarative attributes and the behavioral attributes. The device classification service initiates, based on the device type spoofing, a mitigation action regarding the device.

Abstract: In one embodiment, a device obtains information regarding temporary routing patches applied to a network. Each temporary routing patch implements a routing change in the network for a specified amount of time to avoid or mitigate against a service level agreement violation. The device evaluates, using the information regarding the temporary routing patches applied to the network, a plurality of replay scenarios for the network. The device determines, based on the plurality of replay scenarios, a long-term configuration change for the network. The device provides an indication of the long-term configuration change for display.

Abstract: In one embodiment, a device obtains a first set of measurements of a path metric for a path in a network that are measured using periodic probing of the path. The device obtains a second set of measurements of the path metric for the path that are measured using fine-grained probing of the path at a higher frequency than that of the periodic probing. The device generates a predictive model that predicts values of the path metric, based on the first set of measurements and on the second set of measurements. The device causes, based on a value of the path metric predicted by the predictive model, traffic to be rerouted from the path to another path in the network.

Abstract: In various embodiments, a device classification service obtains traffic telemetry data for a plurality of devices in a network. The service applies clustering to the traffic telemetry data, to form device clusters. The service generates a device classification rule based on a particular one of the device clusters. The service receives feedback from a user interface regarding the device classification rule. The service adjusts the device classification rule based on the received feedback.

Abstract: In one embodiment, a device classification service obtains telemetry data for a plurality of devices in a network. The device classification service repeatedly assigns the devices to device clusters by applying clustering to the obtained telemetry data. The device classification service determines a measure of stability loss associated with the cluster assignments. The measure of stability loss is based in part on whether a device is repeatedly assigned to the same device cluster. The device classification service determines, based on the measure of stability loss, that the cluster assignments have stabilized. The device classification service obtains device type labels for the device clusters, after determining that the cluster assignments have stabilized.

Abstract: In one embodiment, a device deploys a first machine learning model to an inference location in a network. The first machine learning model is used at the inference location to make inferences about the network. The device receives, from the inference location, an indication that the first machine learning model is exhibiting poor performance. The device identifies a corrective measure for the poor performance that minimizes resource consumption by a model training pipeline of the device. The device deploys, based on the corrective measure, a second machine learning model to the inference location. The second machine learning model is used in lieu of the first machine learning model to make the inferences about the network.

Abstract: In one embodiment, a service receives telemetry data indicative of a plurality of performance metrics captured in a network. The service jointly trains, using the received telemetry data, a compression model and an inference model, the compression model being a first machine learning model trained to convert the telemetry data into a compressed representation of the telemetry data and the inference model being a second machine learning model trained to take the compressed representation of the telemetry data as input and apply a classification label to it. The service deploys the compression model to the network. The service receives compressed telemetry data generated by the compression model deployed to the network. The service uses the inference model to classify the compressed telemetry data generated by the compression model deployed to the network.

Abstract: In one embodiment, a device calculates one or more distributions of bitrates associated with an application whose traffic is conveyed via one or more paths in a network. The device detects throughput modes of the application, based on the one or more distributions of bitrates associated with the application. The device associates each throughput mode with a quality of experience label, to form a plurality of pairs of throughput modes and quality of experience labels. The device estimates a quality of experience metric for the application, based on a bitrate of the application and the plurality of pairs of throughput modes and quality of experience labels.

Abstract: In various embodiments, a device classification service obtains data indicative of device attributes of a plurality of devices. The device classification service forms, based on the obtained data indicative of the device attributes, a concept graph that comprises nodes that represent different sets of the device attributes. The device classification service determines, by analyzing the concept graph, a relevance score for each of the device attributes that quantifies how relevant that attribute is to classifying a device by its device type. The device classification service uses the relevance scores for the device attributes to cluster the plurality of devices into device type clusters by their device attributes.

Abstract: In various embodiments, a device classification service uses an initial device classification rule to label each of a set of endpoint devices in a network as being of a particular device type. The device classification service identifies a particular attribute exhibited by at least a portion of the set of endpoint devices and was not previously used to generate the initial device classification rule. The device classification service generates one or more new device classification rules based in part on the particular attribute. The device classification service switches from using the initial device classification rule to label endpoint devices in the network to using the one or more new device classification rules to label endpoint devices in the network.

Abstract: In one embodiment, a device obtains traffic telemetry data regarding a first path in a network and an alternate path in the network. The device predicts, based on the traffic telemetry data, an amount of traffic for an application that is expected at a particular time. The device makes, based on the traffic telemetry data and on the amount of traffic for the application that is predicted to be expected at the particular time, a counterfactual prediction as to whether the alternate path would violate a service level agreement associated with the traffic, should the traffic be routed via the alternate path at the particular time. The device causes, based on the counterfactual prediction, the traffic for the application to be rerouted from the first path in the network to the alternate path, prior to the particular time.

Abstract: In one embodiment, a device calculates one or more distributions of bitrates associated with an application whose traffic is conveyed via one or more paths in a network. The device detects throughput modes of the application, based on the one or more distributions of bitrates associated with the application. The device associates each throughput mode with a quality of experience label, to form a plurality of pairs of throughput modes and quality of experience labels. The device estimates a quality of experience metric for the application, based on a bitrate of the application and the plurality of pairs of throughput modes and quality of experience labels.

Abstract: In various embodiments, a device classification service makes a determination that an endpoint device in a network is eligible for expedited device classification based on a policy. The device classification service obtains, after making the determination that the endpoint device in the network is eligible for expedited device classification, telemetry data regarding the endpoint device generated by actively probing the endpoint device. The device classification service determines whether the telemetry data regarding the endpoint device matches any existing device classification rules. The device classification service generates, based on the telemetry data, a device classification rule that assigns a device type to the endpoint device, when the telemetry data does not match any existing device classification rules.

Abstract: In various embodiments, a device classification service obtains traffic telemetry data for a plurality of devices in a network. The service applies clustering to the traffic telemetry data, to form device clusters. The service generates a device classification rule based on a particular one of the device clusters. The service receives feedback from a user interface regarding the device classification rule. The service adjusts the device classification rule based on the received feedback.

Abstract: In one embodiment, a device classification service forms a device cluster by applying clustering to telemetry data associated with a plurality of devices. The service obtains device type labels for the device cluster. The service generates a device type classification rule using the device type labels and the telemetry data. The service determines whether the device type classification rule should be revalidated by applying a revalidation policy to the device type classification rule. The service revalidates the device type classification rule, based on a determination that the device type classification rule should be revalidated.

Abstract: In one embodiment, a service receives a plurality of device type classification rules, each rule comprising a device type label and one or more device attributes used as criteria for application of the label to a device in a network. The service estimates, across a space of the device attributes, device densities of devices having device attributes at different points in that space. The service uses the estimated device densities to identify two or more of the device type classification rules as having overlapping device attributes. The service determines that the two or more device type classification rules are in conflict, based on the two or more rules having different device type labels. The service generates a rule conflict resolution that comprises one of the device type labels from the conflicting two or more device type classification rules.