Abstract: In one embodiment, a service tracks performance of a machine learning model over time. The machine learning model is used to monitor one or more computer networks based on data collected from the one or more computer networks. The service also tracks performance metrics associated with training of the machine learning model. The service determines that a degradation of the performance of the machine learning model is anomalous, based on the tracked performance of the machine learning model and performance metrics associated with training of the model. The service initiates a corrective measure for the degradation of the performance, in response to determining that the degradation of the performance is anomalous.

Abstract: In one embodiment, a device makes, using a prediction model, a prediction regarding a quality of experience metric for an online application, based on telemetry from a network used to access the network. The device determines a degree of uncertainty associated with the prediction. The device selects one or more users of the online application from which user feedback should be requested, based on the degree of uncertainty. The device requests that the one or more users provide user feedback regarding their satisfaction with the online application.

Abstract: In one embodiment, a device receives, at a first large language model executed by a device, textual input from a user of a network regarding a networking issue in the network. The device issues, by the first large language model and to a second large language model, one or more questions regarding the network based on the textual input. The device receives, at the first large language model and from the second large language model, one or more answers to the one or more questions. The device generates, by the first large language model, a textual response to the textual input for presentation to the user.

Abstract: In one embodiment, a device identifies, based on traceroute information for a path in a network between an endpoint client and an online application, a particular segment of the path as most likely to cause degraded performance along the path. The device makes, using a prediction model, a prediction that routing traffic for the online application via the path will result in degraded quality of experience for the online application. The device obtains, based on the prediction, additional traceroute information in the network, to identify a bypass path in the network between the endpoint client and the online application that bypasses the particular segment. The device causes traffic for the online application to be routed along the bypass path.

Abstract: In one embodiment, a device obtains characteristics of a first anomaly detection model executed by a first distributed learning agent in a network. The device receives a query from a second distributed learning agent in the network that requests identification of a similar anomaly detection to that of a second anomaly detection model executed by the second distributed learning agent. The device identifies, after receiving the query from the second distributed learning agent, the first anomaly detection model as being similar to that of the second anomaly detection model, based on the characteristics of the first anomaly detection model. The device causes the first anomaly detection model to be sent to the second distributed learning agent for execution.

Abstract: In one embodiment, a device identifies a plurality of online applications accessible via a network for which a prediction model was trained to predict their application experiences. The device makes a determination as to whether a particular online application is behaviorally similar to any of the plurality of online applications. The device obtains, based on the determination, application experience metrics for the particular online application, when the particular online application is not behaviorally similar to any of the plurality of online applications. The device trains, using the application experience metrics for the particular online application, the prediction model to predict an application experience of the particular online application in addition to those of the plurality of online applications, when the particular online application is not behaviorally similar to any of the plurality of online applications.

Abstract: In one embodiment, a device obtains trace data indicative of application programming interface calls made during sessions between clients and an application accessible via a network. The device identifies, based on the trace data, a set of calls among the application programming interface calls that are frequently co-occurring. The device quantifies a latency savings expected to occur were the set of calls to be consolidated into a singular call instead of being made separately. The device provides, to a user interface, a recommendation to consolidate the set of calls into a consolidated call that indicates the latency savings.

Abstract: In one embodiment, a device maps network addresses associated with a user to a plurality of geographical locations, at least one of which is an office location of an enterprise. The device obtains quality of experience metrics for an online application accessed by the user. The device makes, based on the quality of experience metrics, a determination as to whether the office location of the enterprise would afford better application experience to the user. The device provides an indication of the determination for display.

Abstract: In one embodiment, a device identifies a plurality of online applications accessible via a network for which a prediction model was trained to predict their application experiences. The device makes a determination as to whether a particular online application is behaviorally similar to any of the plurality of online applications. The device obtains, based on the determination, application experience metrics for the particular online application, when the particular online application is not behaviorally similar to any of the plurality of online applications. The device trains, using the application experience metrics for the particular online application, the prediction model to predict an application experience of the particular online application in addition to those of the plurality of online applications, when the particular online application is not behaviorally similar to any of the plurality of online applications.

Abstract: In one embodiment, a device obtains telemetry data associated with a particular online application accessed via a wireless network. The device computes statistics regarding traffic associated with the particular online application in the wireless network. The device generates a wireless configuration for an access point of the wireless network based on the telemetry data and the statistics. The device provides the wireless configuration for use by the access point when communicating with a client of the particular online application.

Abstract: In one embodiment, a device identifies available resources of a tunnel in a first software defined network. The device provides, based on the available resources, an indication that the tunnel is available to convey traffic sent by a second software defined network. The device receives, based on the indication, a request to convey traffic sent by the second software defined network via the tunnel in the first software defined network. The device configures a peering node in the first software defined network to connect the second software defined network to the tunnel to allow the traffic sent by the second software defined network to be conveyed via the tunnel.

Abstract: In one embodiment, a device initiates, using a chaos engineering tool, performance of randomized actions in a network via which an online application is accessible. The device obtains telemetry data from the network. The device computes correlations between the telemetry data and the randomized actions. The device uses the correlations to determine a root cause of an event in the network with respect to the online application.

Abstract: In one embodiment, a device obtains a plurality of telemetry metrics regarding an online application accessed via a network. The device trains, based on the plurality of telemetry metrics, a gradient boosted tree-based prediction model to make predictions regarding a quality of experience for the online application. The device quantifies how influential a particular telemetry metric is on the predictions. The device provides a visualization tool for display that indicates how influential the particular telemetry metric is on the predictions.

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 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 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 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.