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 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.

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 predicts, for each of a plurality of applications accessible via a network, quality metrics for different network paths where traffic for that application be routed via one or more paths among the different network paths. The device generates a congestion risk prediction model that predicts a risk of traffic congestion for a particular combination of: applications from among the plurality of applications, traffic flows associated with those applications, and paths among the network paths via which those traffic flows may be routed. The device performs a constrained optimization based on the predicted quality metrics and on the risk of traffic congestion predicted by the model, to assign traffic flows for the applications to a selected subset of the different paths. The device causes the traffic flows to be routed in the network via the selected subset of the different paths to which they are assigned.

Abstract: In one embodiment, a device obtains probe results generated by probing a plurality of entities in a network from a first node in the network. The device generates a location identifier for the first node that represents its location in the network, based on the probe results. The device selects a configuration associated with the first node for use by a second node in the network, based on a difference between the location identifier for the first node and a location identifier for the second node. The device causes the configuration to be deployed to the second node.

Abstract: In one embodiment, a first networking device in a network coordinates, with a second networking device in the network, capture of packet maps for a traffic flow in the network associated with a particular application. The packet maps comprise multi-dimensional histograms indexed by identified properties of packets of the traffic flow and time. The first networking device inspects packets of the traffic flow, to identify properties of packets of the traffic flow. The first networking device generates a first packet map for the traffic flow based on the properties of the packets of the traffic flow identified by the first networking device. The first networking device causes a comparison between the first packet map and a second packet map generated by the second networking device to be used as a measure of application experience for the particular application.

Abstract: In one embodiment, a device obtains quality of experience measurements for an online application having application traffic conveyed via a first path in a backbone network between an ingress point and an egress point in the backbone network. The device makes a determination that the quality of experience metrics are indicative of degraded user experience with the online application. The device causes a label switched path to be scheduled between the ingress point and the egress point in the backbone network. The device causes at least a portion of the application traffic for the online application to be sent via the label switched path instead of the first path.

Abstract: In one embodiment, a device identifies a potential change in user experience of an online application. The device selects, based on the potential change in user experience, a set of one or more users of the online application. The device obtains, from the set of one or more users of the online application, feedback regarding their experience with the online application. The device uses the feedback obtained from the set of one or more users of the online application to make a routing decision in a network regarding traffic of the online application.

Abstract: In one embodiment, a device receives observed access point (AP) features of one or more APs in a monitored network. The device clusters the observed AP features within a latent space to form AP feature clusters. The device applies labels to the AP feature clusters within the latent space. The device uses the applied labels to the AP feature clusters to describe future behaviors of the one or more APs in the monitored network.

Abstract: In one embodiment, a method comprises identifying, by a path computation element, essential parent devices from a nonstoring destination oriented directed acyclic graph (DODAG) topology as dominating set members belonging to a dominating set; receiving, by the path computation element, an advertisement message specifying a first dominating set member having reachability to a second dominating set member, the reachability distinct from the nonstoring DODAG topology; and generating, by the path computation element based on the advertisement message, an optimized path for reaching a destination network device in the nonstoring DODAG topology via a selected sequence of dominating set members, the optimized path providing cut-through optimization across the nonstoring DODAG topology.

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 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 predicts, for each of a set of paths via which traffic for an online application can be routed, a distribution of an application experience metric for the online application. The device computes, for different subsets of the set of paths, aggregated distributions of their distributions of the application experience metric predicted by the device. The device makes comparisons between the aggregated distributions for the different subsets of the set of paths. The device causes, based on the comparisons, the traffic for the online application to be routed via a particular subset of the set of paths.

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 generates state trajectories from the states of the network path computed by the device. The device selects one or more sub-sequences of the state trajectories based on prediction performance metrics that represent how well the one or more sub-sequences are able to predict a failure condition of the network path. The device causes a networking entity to use the one or more sub-sequences of the state trajectories to perform predictive routing for the network path.

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 one embodiment, a service receives telemetry data collected from a plurality of different networks. The service combines the telemetry data into a synthetic input trace. The service inputs the synthetic input trace into a plurality of machine learning models to generate a plurality of predicted key performance indicators (KPIs), each of the models having been trained to assess telemetry data from an associated network in the plurality of different networks and predict a KPI for that network. The service compares the plurality of predicted KPIs to identify one of the plurality of different networks as exhibiting an abnormal behavior.

Abstract: In one embodiment, a device predicts, for each of a set of paths via which traffic for an online application can be routed, a distribution of an application experience metric for the online application. The device computes, for different subsets of the set of paths, aggregated distributions of their distributions of the application experience metric predicted by the device. The device makes comparisons between the aggregated distributions for the different subsets of the set of paths. The device causes, based on the comparisons, the traffic for the online application to be routed via a particular subset of the set of paths.

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 obtains path metrics for a network path via which traffic for an online application is conveyed. The device models uncertainty of an application experience metric predicted for the online application based on the path metrics. The device identifies, based on the uncertainty of the application experience metric modeled by the device, a breaking point in the path metrics at which the application experience metric predicted for the online application is expected to switch from being acceptable to being degraded. The device provides the breaking point in the path metrics for display.

Abstract: In one embodiment, a device obtains client attribute data for clients of an online application that access the online application via a plurality of points of presence in a network. The device forms a performance model that models an application experience metric for the online application as a function of the client attribute data for each of the plurality of points of presence. The device selects, using the performance model, a particular point of presence from among the plurality of points of presence to be used by a particular client to access the online application, based on its client attribute data. The device causes the particular client to access the online application via the particular point of presence selected by the device using the performance model.