Network capacity is provisioned in a computing environment comprising a computing service provider and an edge computing network. A cost function is applied to usage data for a number of user endpoints at the edge computing network, a number and type of workloads at the edge computing network, offload capability of the edge computing network, and resource capacities at the edge computing network. An estimated network capacity is determined, where the workloads are dynamic, and the cost function is usable to optimize the network capacity with respect to one or more criteria.

Network capacity is provisioned in a computing environment comprising a computing service provider and an edge computing network. A cost function is applied to usage data for a number of user endpoints at the edge computing network, a number and type of workloads at the edge computing network, offload capability of the edge computing network, and resource capacities at the edge computing network. An estimated network capacity is determined, where the workloads are dynamic, and the cost function is usable to optimize the network capacity with respect to one or more criteria.

Disclosed is a technique that can be performed by a server computer system. The technique can include obtaining data from each of multiple endpoint devices to form global data. The global data can be generated by the endpoint devices in accordance with local instructions in each of the endpoint devices. The technique further includes generating global instructions based on the global data and sending the global instructions to a particular endpoint device. The global instructions configure the particular endpoint device to perform a data analytic operation that analyzes events. The events can include raw data generated by a sensor of the particular endpoint device.

Disclosed is a technique that can be performed by a server computer system. The technique can include obtaining data from each of multiple endpoint devices to form global data. The global data can be generated by the endpoint devices in accordance with local instructions in each of the endpoint devices. The technique further includes generating global instructions based on the global data and sending the global instructions to a particular endpoint device. The global instructions configure the particular endpoint device to perform a data analytic operation that analyzes events. The events can include raw data generated by a sensor of the particular endpoint device.

Methods and systems for partially or fully distributed network verification are described. In partially distributed network verification, each network device generates a respective device-level binary decision diagram (BDD) representing the logical behavior of the respective network device for a network property of interest. The device-level BDDs from each network device are received by a verification service that performs verification by generating an input BDD representing an input header space, and applies each device-level BDD in a logical path from a source device to a destination device, and reports the output BDD. In fully distributed network verification, each network device is responsible for calculating a device-specific output BDD by applying a device-specific BDD, which represents the logical behavior of the network device, to a device-specific input BDD.

An information processing method includes: obtaining, by a data analytics network element, terminal behavioral information of a plurality of terminals; determining, by the data analytics network element, network-side expected terminal behavioral information based on the terminal behavioral information; and sending, by the data analytics network element, the network-side expected terminal behavioral information to a user data management network element.

Disclosed are systems, methods, and computer-readable media for assuring tenant forwarding in a network environment. Network assurance can be determined in layer 1, layer 2 and layer 3 of the networked environment including, internal-internal (e.g., inter-fabric) forwarding and internal-external (e.g., outside the fabric) forwarding in the networked environment. The network assurance can be performed using logical configurations, software configurations and/or hardware configurations.

Methods and systems for autonomous computing comprising processing historical data to analyze a past performance, collecting data from a plurality of connected devices over a network, synchronizing the collected data from the plurality of connected devices with the processed historical data. Based on the synchronized data, methods and systems disclosed include detecting an alert (error/fault) condition in one or more of the plurality of connected devices, based on the detected alert condition, triggering the delivery of the detected alert condition to an automated network operations center (NOC), and matching the determined alert condition to a historical alert condition by the network operations center. Based on the matching, methods and systems include determining a corrective action, and based on the determined corrective action, assigning a virtual self-healing module from a plurality of virtual self-healing modules. Finally, a trigger to performance of the determined corrective action by the assigned virtual self-healing module is initiated.

There is provided a method for generating network optimizing information including the steps of identifying system devices that are comprised in a network, collecting metrics from the identified system devices, including collecting at least one metric relating to the operation, status, capability, limitations, expandability, scalability, or performance of the system devices, assessing the collected metrics according to a predetermined assessment protocol, generating a roster of metrics of interest, such metrics of interest being a group of the collected metrics that meet a selection criteria and not including other collected metrics that do not meet the selection criteria, and presenting each of the metrics of interest in a format suitable for a network operator to take corrective actions with regard to the identified non-compliant metrics or to capitalize on the identified optimization opportunities with respect to the network.

The disclosed apparatus, systems, and methods relate to a location query mechanism that can efficiently determine whether a target entity is located within a region of interest (ROI). At a high level, the location query mechanism can be configured to represent a ROI using one or more polygons. The location query mechanism can, in turn, divide (e.g., tessellate) the one or more polygons into sub-polygons. Subsequently, the location query mechanism can use the sub-polygons to build an index system that can efficiently determine whether a particular location is within any of the sub-polygons. Therefore, when a computing device queries whether a particular location is within the region of interest, the location query mechanism can use the index system to determine whether the particular location is within any of the sub-polygons.