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 obtains an indication of a network event predicted by a routing engine for a network. The device initiates monitoring of one or more network paths associated with the network event, to determine one or more states of the network. The device makes a comparison between the one or more states of the network and a set of one or more constraints. The device provides a prediction cancelation notification to the routing engine, based on the comparison.

Abstract: In one embodiment, the device obtains telemetry data for traffic between endpoints in a plurality of local networks and an online application, the telemetry data including local interface data regarding local interfaces used by the endpoints to access the online application and data indicative of which points of presence (PoPs) were traversed by the traffic. The device identifies sessions of the online application in which an application experience metric was degraded. The device determines a root cause of the application experience metric being degraded for the sessions by assessing the telemetry data associated with the sessions. The device provides the root cause to a user interface for presentation.

Abstract: In one embodiment, a device computes, for each of a set of points of presence (PoPs) via which traffic for an online application can be sent from a location, application experience metrics predicted for the application over time. The device assigns, for each of the set of PoPs, weights to different time periods, based on measures of uncertainty associated with the predicted application experience metrics. The device generates, based on the weights assigned to the different time periods for each of the set of PoPs, schedules for probing network paths connecting the location to the online application via those PoPs. The device causes the network paths to be probed in accordance with their schedules. Results of this probing are used to select a particular PoP from among the set of PoPs via which traffic for the online application should be sent from the location during a certain time period.

Abstract: In one embodiment, a device may identify a plurality of impairment scenarios for a network. The device may estimate quality of experience metrics for a plurality of applications accessible via the network for each of the plurality of impairment scenarios. The device may select a particular application from among the plurality of applications based on a comparison between the quality of experience metrics for the plurality of applications. The device may provide an indication for presentation by a user interface that a user should use the particular application from among the plurality of applications.

Abstract: In one embodiment, a device generates an application map for an online application accessed via a network that indicates a set of network addresses at which the online application was accessed, client information for clients that accessed the online application via the network, and quality of experience metrics for the online application. The device identifies a location change of the online application by tracking changes to the application map. The device determines a correlation between the location change and a degradation in the quality of experience metrics. The device adjusts, based on the correlation, routing of traffic associated with the online application in the network.

Abstract: In one embodiment, a device obtains call transcripts from an online application. The device detects cues within the call transcripts that are indicative of poor user experience. The device generates, based in part on the cues detected within the call transcripts, a model trained to predict poor user experience from network path telemetry for the online application. The device causes traffic for the online application to be routed along a particular network path, based on a prediction by the model.

Abstract: In one embodiment, a device computes time series dynamics for a performance metric of a path in a network used to convey traffic for an online application. The device matches those time series dynamics to one or more dynamics categories. The device makes a determination as to whether the path in the network is anomalous, based on the one or more dynamics categories. The device provides, based on the determination, an indication that the path in the network is anomalous for display.

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 generates a plurality of smoothed timeseries by applying smoothing envelopes of different durations to a timeseries of a path metric for a path in a network that is used to convey traffic of an online application. The device uses the plurality of smoothed timeseries and the timeseries of the path metric to make predictions as to whether the path will provide an unacceptable user experience in the online application. The device selects a smoothing envelope of a particular duration, by comparing performance metrics for the predictions. The device uses a timeseries of the path metric smoothed using the smoothing envelope of the particular duration to make predictive routing decisions in the network for the traffic of the online application.

Abstract: In one embodiment, a device provides, to a user interface, a timeseries for display of a probability over time of a network path violating a service level agreement (SLA) associated with an online application. The device receives, from the user interface, a plurality of thresholds for the timeseries that define periods of time during which application experience of the online application is believed to be degraded. The device trains, based on the plurality of thresholds, a machine learning model to predict when the application experience of the online application will be degraded. The device causes a predictive routing engine to reroute traffic of the online application based on a prediction by the machine learning model that the application experience of the online application will be degraded.

Abstract: In one embodiment, a device discovers one or more network interfaces that an endpoint in a local network could use to access an online application. The device identifies a plurality of connectivity options available to the endpoint to access the online application via an external network. The device makes a prediction that a path that comprises a particular connectivity option from among the plurality of connectivity options and a particular network interface from among the one or more network interfaces will provide an optimal quality of experience metric associated with the online application. The device causes the endpoint to use the path to connect to the online application.

Abstract: In one embodiment, a device identifies a plurality of online applications whose traffic is conveyed via an interface of a networking entity of a network. The device computes a relationship between a quality of experience metric for a particular online application in the plurality of online applications and a traffic volume associated with the interface for the plurality of online applications. The device makes, based on the relationship, a determination that degradation of the quality of experience metric for the particular online application is due to the traffic volume associated with the interface for the plurality of online applications. The device reconfigures, based on the determination, the networking entity to prioritize traffic for the particular online application.

Abstract: In one embodiment, a device generates an application map for an online application accessed via a network that indicates a set of network addresses at which the online application was accessed, client information for clients that accessed the online application via the network, and quality of experience metrics for the online application. The device identifies a location change of the online application by tracking changes to the application map. The device determines a correlation between the location change and a degradation in the quality of experience metrics. The device adjusts, based on the correlation, routing of traffic associated with the online application in the network.

Abstract: In one embodiment, a device identifies peaks of a timeseries of a path metric used to predict performance of a path in a network. The device determines one or more characteristics of the peaks of the timeseries. The device computes, based on the one or more characteristics of the peaks, a measurement frequency for the path metric. The device causes the path metric to be measured in the network according to the measurement frequency.

Abstract: In one embodiment, a device obtains perception results generated by one or more perception models that use media data as input that is transmitted between endpoints of an online application via a network. The device computes performance measures for the one or more perception models, based in part on the perception results and on the media data. The device quantifies, based on the performance measures, quality of experience for the online application. The device causes a configuration change to be made with respect to the online application, based on the quality of experience.

Abstract: In one embodiment, a device obtains quality of experience metrics for an online application whose traffic traverses a particular interface of a router located at a first site in a network. The device identifies a correlation between throughput of the particular interface and the quality of experience metrics for the online application. The device makes a determination that the correlation is a root cause of degradation of the quality of experience metrics for the online application at least in part by determining whether throughput of an interface of a remote router located at a second site in the network is correlated with the quality of experience metrics. The device configures, based on the determination, a priority queue associated with the particular interface for use by traffic of the online application.

Abstract: In one embodiment, a device generates a plurality of smoothed timeseries by applying smoothing envelopes of different durations to a timeseries of a path metric for a path in a network that is used to convey traffic of an online application. The device uses the plurality of smoothed timeseries and the timeseries of the path metric to make predictions as to whether the path will provide an unacceptable user experience in the online application. The device selects a smoothing envelope of a particular duration, by comparing performance metrics for the predictions. The device uses a timeseries of the path metric smoothed using the smoothing envelope of the particular duration to make predictive routing decisions in the network for the traffic of the online application.

Abstract: In one embodiment, a device obtains telemetry data associated with application traffic for an online application. The device identifies, based on the telemetry data, a network path as potentially exhibiting transient events during which a performance metric of the network path is degraded that are undetected at a first probing frequency for the network path. The device determines a new probing frequency for the network path, to detect the transient events. The device causes probes to be sent along the network path according to the new probing frequency.

Abstract: In one embodiment, a device obtains media data captured by an endpoint of an online application that results from conducting a test in a network that subjects traffic of the online application to a particular network path degradation pattern. The device receives user feedback regarding the media data from one or more user interfaces. The device computes, based on the user feedback, a quality of experience metric for the online application associated with the particular network path degradation pattern. The device causes a configuration change to be made with respect to the online application, based on the quality of experience metric.