Abstract: In one embodiment, a device receives application experience metrics for a software-as-a-service application. The device generates, based on the application experience metrics, a predictive model that predicts application experience scores for a plurality of network service providers that provide connectivity to the software-as-a-service application. The device selects a particular network service provider for use by a location, based on an application experience score predicted by the predictive model. The device sends an indication of the particular network service provider to the location.

Abstract: In one embodiment, a device uses a classification model to determine whether implementation of a routing change suggested by a predictive routing engine for a network will result in a violation of one or more network policies. The device computes a trust score, based on performance metrics for the classification model. The device causes, based in part on the trust score, implementation of the routing change in the network, when the classification model determines that application of the routing change will not result in a violation of the one or more network policies.

Abstract: In one embodiment, a device obtains routing forecasts for a software defined network. The device splits a particular routing policy for the software defined network into two or more routing policies, based on the routing forecasts. The device makes an evaluation as to whether the two or more routing policies should be reverted back into the particular routing policy. The device sends, to a user interface, data indicative of the particular routing policy that was split into the two or more routing policies and the evaluation as to whether the two or more routing policies should be reverted back into the particular routing policy.

Abstract: In various embodiments, a device classification service obtains traffic telemetry data for a plurality of devices in a network. The service applies clustering to the traffic telemetry data, to form device clusters. The service generates a device classification rule based on a particular one of the device clusters. The service receives feedback from a user interface regarding the device classification rule. The service adjusts the device classification rule based on the received feedback.

Abstract: In one embodiment, a controller obtains data indicative of an application experience metric for an online application having application traffic conveyed via the network. The controller predicts the application experience metric that would result from a first edge router conveying its application traffic to the online application via a second edge router that is not currently connected to the first edge router via a tunnel, based on the obtained data. The controller makes a determination that the first edge router should route its application traffic to the online application via a tunnel between the first edge router and the second edge router, based on the predicted application experience metric. The controller causes a tunnel to be established in the network between the first edge router and the second edge router, whereby the first edge router routes its application traffic to the online application via the second edge router.

Abstract: In one embodiment, a device predicts a failure of a first tunnel in a software-defined wide area network (SD-WAN). The device determines that no backup tunnel for the first tunnel exists in the SD-WAN that can satisfy one or more service level agreements (SLAs) of traffic on the first tunnel, were the traffic rerouted from the first tunnel onto that tunnel. The device predicts, using a machine learning model, that a backup tunnel for the first tunnel exists in the SD-WAN that can satisfy an SLA of a subset of the traffic on the first tunnel, in response to determining that no backup tunnel exists in the SD-WAN that can satisfy the one or more SLAs of the traffic on the first tunnel. The device proactively reroutes the subset of the traffic on the first tunnel onto the backup tunnel, in advance of the predicted failure of the first tunnel.

Abstract: A cart is disclosed which is capable of being wheeled to a vehicle and mounted thereon through a mount secure coupling attached to the cart and a secure receiver attached to the vehicle. Upon secure attachment to the vehicle, the carts undercarriage assembly including wheels are capable of being retracted into the top deck assembly of the cart. Therefore, the vehicle maintains clearance from the road even after the cart is attached. Upon reaching a destination, the undercarriage of the cart is lowered and the cart is detached from the vehicle. The cart is then able to be wheeled away. The cart allows for transportation of loads without the need for the operator to actually carry either the load or the cart at any stage from start to finish or perform any additional steps to safe and securely attach the cart to the vehicle.

Abstract: A method for generating a composite visibility indicator of at least one entity includes selecting a first category of entities; selecting a first position; associating the first position with a first geographical zone, said first geographical zone defining a zone around the first position as a function of the selected category; generating at least one request to collect a plurality of indicators, and generating a composite visibility indicator for each entity of the first category selected within the first zone, the visibility indicator being a function of the first indicator and of the second indicator.

Abstract: In one embodiment, a supervisory service for a network obtains a first set of user experience survey data from users of an online application whose traffic was rerouted by a predictive routing engine. The supervisory service obtains a second set of user experience survey data from users of the online application whose traffic was not rerouted by the predictive routing engine. The supervisory service generates, using the first set of user experience survey data and the second set of user experience survey data, a user experience prediction model to predict user experience scores for the online application. The supervisory service adjusts the predictive routing engine using predicted user experience scores from the user experience prediction model.

Abstract: In one embodiment, a device classification service obtains telemetry data for a plurality of devices in a network. The device classification service repeatedly assigns the devices to device clusters by applying clustering to the obtained telemetry data. The device classification service determines a measure of stability loss associated with the cluster assignments. The measure of stability loss is based in part on whether a device is repeatedly assigned to the same device cluster. The device classification service determines, based on the measure of stability loss, that the cluster assignments have stabilized. The device classification service obtains device type labels for the device clusters, after determining that the cluster assignments have stabilized.

Abstract: In one embodiment, a controller obtains data indicative of an application experience metric for an online application having application traffic conveyed via the network. The controller predicts the application experience metric that would result from a first edge router conveying its application traffic to the online application via a second edge router that is not currently connected to the first edge router via a tunnel, based on the obtained data. The controller makes a determination that the first edge router should route its application traffic to the online application via a tunnel between the first edge router and the second edge router, based on the predicted application experience metric. The controller causes a tunnel to be established in the network between the first edge router and the second edge router, whereby the first edge router routes its application traffic to the online application via the second edge router.

Abstract: In one embodiment, a device obtains routing forecasts for a software defined network. The device splits a particular routing policy for the software defined network into two or more routing policies, based on the routing forecasts. The device makes an evaluation as to whether the two or more routing policies should be reverted back into the particular routing policy. The device sends, to a user interface, data indicative of the particular routing policy that was split into the two or more routing policies and the evaluation as to whether the two or more routing policies should be reverted back into the particular routing policy.

Abstract: In one embodiment, a device deploys a first machine learning model to an inference location in a network. The first machine learning model is used at the inference location to make inferences about the network. The device receives, from the inference location, an indication that the first machine learning model is exhibiting poor performance. The device identifies a corrective measure for the poor performance that minimizes resource consumption by a model training pipeline of the device. The device deploys, based on the corrective measure, a second machine learning model to the inference location. The second machine learning model is used in lieu of the first machine learning model to make the inferences about the network.

Abstract: In a convergent-divergent flap pair for a turbojet engine nozzle of the variable-geometry convergent-divergent type, the convergent flap and the divergent flap including respective ducts for circulation of cooling air connected to one another to allow cooling of each of the flaps. The duct of the divergent flap includes an impingement cooling cavity defined by two walls of lateral ends provided with air passage orifices through which the impingement cooling cavity opens towards the outside.

Abstract: A computer implemented method is presented for querying a dataset that contains sensitive attributes. The method comprises the steps of receiving a query specification, generating a set of aggregate statistics derived from the sensitive dataset based on the query specification and encoding the set of aggregate statistics using a set of linear equations. The relationships of each sensitive attribute represented in the set of aggregate statistics are also encoded into the set of linear equations.

Abstract: In one embodiment, a device identifies a set of probes configured between a first endpoint and a second endpoint serving an online application. Each probe has one or more characteristics and is associated with a different segment between the endpoints. The device selects a subset of the set whose associated segments are along a plurality of paths between the endpoints, based on a match between the online application and the one or more characteristics of probes in the set of probes. The device approximates a performance metric for each of the plurality of paths by aggregating performance metrics measured by probes in the subset of probes that are associated with segments of that path. The device causes traffic to be routed between the endpoints via a particular path in the plurality of paths, based on the performance metric of the particular path.

Abstract: In various embodiments, a device classification service obtains data indicative of device attributes of a plurality of devices. The device classification service forms, based on the obtained data indicative of the device attributes, a concept graph that comprises nodes that represent different sets of the device attributes. The device classification service determines, by analyzing the concept graph, a relevance score for each of the device attributes that quantifies how relevant that attribute is to classifying a device by its device type. The device classification service uses the relevance scores for the device attributes to cluster the plurality of devices into device type clusters by their device attributes.

Abstract: In one embodiment, a service receives software version data regarding versions of software executed by devices in a network. The service detects a version change in the version of software executed by one or more of the devices, based on the received software version data. The service makes a determination that a drop in data quality of input data for a machine learning model used to monitor the network is associated with the detected version change. The service reverts the one or more devices to a prior version of software, based on the determination that the drop in quality of the input data for the machine learning model used to monitor the network is associated with the detected version change.

Abstract: A method and apparatus for artificial neural network precoding for massive MIMO systems are disclosed. In one embodiment, a method includes processing, by an artificial neural network, ANN, precoding engine, at least one input signal by 5 performing a low peak-to-average-power ratio, PAPR, precoding on the at least one input signal; and transmitting, via at least one antenna array having at least one antenna, at least one precoded output signal processed by the ANN precoding engine.

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.