Abstract: Some embodiments provide a novel method for assessing the suitability of network links for connecting compute nodes located at different geographic sites. The method of some embodiments identifies and analyzes sample packets from a set of flows exchanged between first and second compute sites that are connected through a first network link in order to identify attributes of the sampled packets. The method also computes attributes of predicted packets between the identified samples in order to identify attributes of each flow in the set of flows. The method then uses the identified and computed attributes of each flow in the set of flows to emulate the set of flows passing between the two compute sites through the second network link in order to assess whether a second network link should be used for future flows (e.g., future flows exchanged between the first and second compute sites).

Abstract: A method, a device, and a non-transitory storage medium each provide an object recognition service that identifies an object within an image or a video. An object is identified according to a general classification based on a domain-based inference model, and is subsequently further identified according to a sub-classification of the general classification based on another domain-based inference model. The domain-based inference models are hierarchical. The object recognition of the object may be used in support of end user services.

Abstract: In one embodiment, a method includes receiving traffic and identifying one or more attributes associated with the traffic. The method also includes dynamically selecting a load balancing algorithm based on the one or more attributes in accordance with a load balancing scheme. The method further includes performing load balancing on the traffic in accordance with the load balancing algorithm and communicating the traffic from a first network element to a second network element in accordance with the load balancing.

Abstract: One or more computing devices, systems, and/or methods for calibrating barometric sensors and/or determining altitudes of devices are provided. In an example, one or more barometric pressure measures are determined using a barometric sensor of a device. One or more locations of the device are determined based upon one or more global navigation satellite system (GNSS) signals and one or more corrective signals associated with the one or more GNSS signals. One or more reference values are determined based upon the one or more locations. A barometric offset is determined based upon the one or more barometric pressure measures and the one or more reference values. A first barometric measurement is performed using the barometric sensor to determine a first barometric pressure measure. An adjusted barometric pressure measure and/or an altitude of the device are determined based upon the first barometric pressure measure and the barometric offset.

Abstract: An efficient method to handle fragmented packets in multi-node all-active clusters. In one particular embodiment, a method includes receiving an initial fragment packet at a node in a cluster, creating a secondary flow table, linking the secondary flow table to a primary flow table, determining the primary flow owner of the initial fragment packet, and transmitting initial and succeeding fragment packets out of the cluster through, if possible, the primary flow owner.

Abstract: One or more computing devices, systems, and/or methods for elevation tracking of devices are provided. Barometric pressure data points and elevation data points associated with a set of barometric measurement devices proximate a device are obtained. An interpolated field of barometric pressure is generated using the barometric pressure data points and the elevation data points. x, y location coordinates of the device are projected onto the interpolated field as a projected point within the interpolated field. Barometric leveling is executed upon a device barometric pressure value provided by the device and a barometric pressure data point at the projected point to determine a relative height difference between the device and the projected point. An elevation of the device is determined based upon the relative height difference.

Abstract: One or more computing devices, systems, and/or methods for calibrating barometric sensors and/or determining altitudes of devices are provided. In an example, one or more barometric pressure measures are determined using a barometric sensor of a device. One or more locations of the device are determined based upon one or more global navigation satellite system (GNSS) signals and one or more corrective signals associated with the one or more GNSS signals. One or more reference values are determined based upon the one or more locations. A barometric offset is determined based upon the one or more barometric pressure measures and the one or more reference values. A first barometric measurement is performed using the barometric sensor to determine a first barometric pressure measure. An adjusted barometric pressure measure and/or an altitude of the device are determined based upon the first barometric pressure measure and the barometric offset.

Abstract: A method, a device, and a non-transitory storage medium each provide an object recognition service that identifies an object within an image or a video. An object is identified according to a general classification based on a domain-based inference model, and is subsequently further identified according to a sub-classification of the general classification based on another domain-based inference model. The domain-based inference models are hierarchical. The object recognition of the object may be used in support of end user services.

Abstract: A method, a device, and a non-transitory storage medium each provide an object recognition service that identifies an object within an image or a video. An object is identified according to a general classification based on a domain-based inference model, and is subsequently further identified according to a sub-classification of the general classification based on another domain-based inference model. The domain-based inference models are hierarchical. The object recognition of the object may be used in support of end user services.

Abstract: A method, a device, and a non-transitory storage medium each provide an object recognition service that identifies an object within an image or a video. An object is identified according to a general classification based on a domain-based inference model, and is subsequently further identified according to a sub-classification of the general classification based on another domain-based inference model. The domain-based inference models are hierarchical. The object recognition of the object may be used in support of end user services.