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 obtains telemetry data associated with an online application accessible via a network. The device trains, based on the telemetry data, a machine learning model to determine whether a given address in the network is associated with the online application. The device uses the machine learning model to generate a listing of network addresses associated with the online application. The device provides the listing of network addresses for use by an application detection service in the network to determine that an initial packet of a traffic flow is associated with the online application based on a match between a destination address of that packet and a network address in the listing of network addresses.

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 obtains telemetry data associated with an online application accessible via a network. The device trains, based on the telemetry data, a machine learning model to determine whether a given address in the network is associated with the online application. The device uses the machine learning model to generate a listing of network addresses associated with the online application. The device provides the listing of network addresses for use by an application detection service in the network to determine that an initial packet of a traffic flow is associated with the online application based on a match between a destination address of that packet and a network address in the listing of network addresses.

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 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 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 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 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.

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 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 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 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 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 telemetry data regarding Domain Name System (DNS) traffic in a network. The device associates, based on the telemetry data, the DNS traffic with a particular online application. The device identifies a traffic pattern of the DNS traffic associated with the particular online application. The device makes, based on the traffic pattern, a determination that an application experience of one or more users of the particular online application is degraded.

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.

Abstract: In one embodiment, a device obtains telemetry data regarding Domain Name System (DNS) traffic in a network. The device associates, based on the telemetry data, the DNS traffic with a particular online application. The device identifies a traffic pattern of the DNS traffic associated with the particular online application. The device makes, based on the traffic pattern, a determination that an application experience of one or more users of the particular online application is degraded.

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 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.