Abstract: In one embodiment, a device obtains interaction data indicative of a flow of interactions performed by a user with an online application accessible via a network. The device detects an anomaly by applying an anomaly detection model to the interaction data. The device determines that the anomaly is attributable to a condition present in the network. The device causes performance of an adjustment to the network, to mitigate the condition.

Abstract: In one embodiment, a device obtains a set of probing motifs. Each probing motif groups similar patterns of path probing results for one or more path metrics in a network. The device generates signatures for the set of probing motifs. Each signature relates a probing strategy with a measure of performance of a classifier to detect that motif were path probing to be conducted in accordance with that probing strategy. The device selects, based on the signatures for the set of probing motifs, a particular probing strategy to use in the network. The device causes one or more probing agents in the network to conduct path probing in accordance with the particular probing strategy.

Abstract: In one embodiment, a device generates a plurality of recommendations for a network, each recommendation indicating a suggested action to optimize quality of experience of a corresponding application accessible via the network. The device assigns scores to different possible groupings of the plurality of recommendations. The device selects a particular grouping from among the plurality of recommendations, based on their scores. The device provides the particular grouping for implementation in the network.

Abstract: In one embodiment, a device receives application performance metrics generated by an online application accessible via a network and indicative of a particular state of the online application. The device performs testing of the online application by replicating a plurality of network impairments. The device associates, based on results of the testing, a particular network impairment from plurality of network impairments with the particular state of the online application. The device provides an indication of the particular network impairment as a cause of the particular state of the online application.

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 determines a mapping between a network destination and Border Gateway Protocol (BGP) peers located across a plurality of autonomous systems for which the network destination is reachable. The device causes, based on the mapping, performance of probing tests along a plurality of paths to the network destination and via the BGP peers, to obtain path performance measurements for the plurality of paths. The device uses a prediction model to generate predicted performance metrics for the plurality of paths based on the path performance measurements. The device configures, based on the predicted performance metrics for the plurality of paths, the BGP peers with BGP peering policies to convey application traffic associated with the network destination via particular path from among the plurality of paths.

Abstract: In one embodiment, a device causes, in accordance with a probing strategy, performance of a probing test by one or more agents in a network and with respect to an online application. The device obtains quality of experience measurements for the online application. The device adjusts, using reinforcement learning, the probing strategy based on how well a predictive model was able to predict the quality of experience measurements given results of the probing test. The device repeats the causing, obtaining, and adjusting steps using the probing strategy adjusted by the device, to find a minimally disruptive probing strategy that provides acceptable performance by the predictive model.

Abstract: In one embodiment, a recommendation service of a device provides a recommended action to a client of an online application predicted to improve a quality of experience metric for the online application. The device receives feedback from the client indicative of the recommended action not being implemented by a user of the client. The device determines, based on the feedback, a reason for the recommended action not being implemented. The device updates the recommendation service based on the reason for the recommended action not being implemented.

Abstract: In one embodiment, a device obtains cross-layer telemetry associated with an online application accessible via a network and from three or more layers of the network. The device estimates a quality of experience metric for the online application using the cross-layer telemetry as input to a cognitive model. The device selects a network action to increase the quality of experience metric estimated by the device. The device causes performance of the network action in the network.

Abstract: In one embodiment, a device obtains quality of experience labels for an online application accessible via a network. The device also obtains telemetry data from the network that is associated with quality of experience labels. The device makes, based on the quality of experience labels and telemetry data, a determination as to whether the online application is similar to a second online application for which a prediction model is already available. The device trains, based on the determination, a prediction model for the online application to predict a quality of experience metric for the online application.

Abstract: In one embodiment, a device forms a latent space by applying dimensionality reduction to timeseries snippets of path metrics for a network path via which traffic for an online application is conveyed. The device extracts a plurality of path states from the latent space. The device presents the plurality of path states for display by a user interface. The device associates a set of transitions between the plurality of path states with the online application providing degraded application experience.

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 application data captured by an intermediate node in a network by inserting monitoring code into traffic associated with one or more online applications accessible via the network. The device identifies, based on the application data, a workflow of actions performed by a user of the one or more online applications. The device determines that a particular stage of the workflow is associated with a quality of experience metric for the one or more online applications being degraded. The device provides a recommended change in the network for presentation by a user interface that is expected to optimize the quality of experience metric for the one or more online applications.

Abstract: In one embodiment, a service in a network computes an expected information gain associated with rerouting traffic from a first tunnel onto a backup tunnel in the network. The service initiates, based on the expected information gain, rerouting of the traffic from the first tunnel onto the backup tunnel. The service obtains performance measurements for the traffic rerouted onto the backup tunnel. The service uses the performance measurements to train a machine learning model to predict whether rerouting traffic from the first tunnel onto the backup tunnel will satisfy a service level agreement (SLA) of the traffic.

Abstract: In one embodiment, a device obtains browser waterfall data from a web browser of a client that is used to access an online application via a network. The device obtains user feedback from the client indicative of whether a user of the client is satisfied with their experience with the online application. The device trains, using the browser waterfall data and user feedback as training data, a prediction model to predict a quality of experience metric for the online application. The device causes an adjustment to the network based on a prediction by the prediction model.

Abstract: In one embodiment, a device obtains a plurality of characteristics of different portions of a network for which a predictive networking engine is available. The device provides the plurality of characteristics of the different portions of a network to a user interface. The device receives, via the user interface, a set of one or more constraints to limit recommendations by the predictive networking engine for a selected portion of the network from among the different portions of the network. The device configures the predictive networking engine to prevent it from generating recommendations for the selected portion of the network according to the set of one or more constraints.

Abstract: In one embodiment, a device makes a determination that user application experience is degraded for a client endpoint of an online application due to a current path in a network that conveys application traffic associated with the online application. A device identifies a bypass path in the network for the application traffic. The device provisions an optimization gateway along the bypass path to forward the application traffic from the client endpoint to the online application. The device causes an endpoint agent executed by the client endpoint to send the application traffic to the optimization gateway for forwarding to the online application via the bypass path.

Abstract: In one embodiment, a device obtains quality of experience metrics specified by a plurality of users of an online application and user information regarding those users. The device identifies, based on the user information, one or more user characteristics of the plurality of users associated with variations in the quality of experience metrics. The device trains a prediction model to predict quality of experience metrics for a subset of the plurality of users based on the one or more user characteristics associated with the subset. The device makes, based on a prediction by the prediction model, an adjustment to a configuration of a network used by the subset to access the online application.

Abstract: In one embodiment, a device associates one or more performance metrics with a particular session of an online application. The device makes a determination that a user of the online application associated with the particular session should be queried for feedback regarding their application experience. The device obtains, based on the determination, feedback from the user regarding their application experience, by causing a chatbot to be presented to the user and query the user for feedback regarding their application experience. The device associates the feedback from the user regarding their application experience with the one or more performance metrics.

Abstract: In one embodiment, a device obtains a set of one or more configuration parameters of an online application accessed by a plurality of clients via a network. The device obtains path information regarding paths in the network via which the plurality of clients accesses the online application. The device determines an updated configuration parameter predicted by a prediction model to increase application experience of the online application based on the path information and the set of one or more configuration parameters. The device provides the updated configuration parameter for use by the online application.