Abstract: Edge clusters execute in a plurality of regional clouds of a cloud computing platforms, which may include cloud POPs. Edge clusters may be programmed to control access to applications executing in the cloud computing platform. A network object is created and represents network resources, such as IP addresses, subnetworks, or virtual networks. Permissions of one or more entities to access the network resources are associated with the network object. Identifiers of entities, including other network resources, are added to the network object to indicate network connections to the network resources. The edge clusters, and possibly one or more virtual networks, are configured by a network orchestrator according to the network object to implement the permissions and network connections.

Abstract: A multiphase digital frequency synthesizer including a multiphase ring oscillator that provides phased clock signals, a clock divider that divides a phased clock signal by an integer value and a carry value to provide a divided clock signal, positive select circuitry that determines and updates a positive select value with accumulation and a modulo function based on a fractional division factor updated with successive cycles of the divided clock signal, carry circuitry that determines the carry value based on a number of the phased clock signals, positive multiplex circuitry that selects from among the phased clock signal using the positive select value for providing a positive multiplexed clock signal, and fractional phase addition circuitry that provides a first output clock signal based on a selected phased clock signal, the divided clock signal, and the positive multiplexed clock signal. Similar negative select circuitry and duty cycle correction circuitry may be included.

Abstract: Devices, computer-readable media and methods are disclosed for selecting paths in reconfigurable optical add/drop multiplexer (ROADM) networks using machine learning. In one example, a method includes defining a feature set for a proposed path through a wavelength division multiplexing network, wherein the proposed path traverses at least one link in the network, and wherein the at least one link connects a pair of reconfigurable optical add/drop multiplexers, predicting an optical performance of the proposed path, wherein the predicting employs a machine learning model that takes the feature set as an input and outputs a metric that quantifies predicted optical performance, and determining whether to deploy a new wavelength on the proposed path based on the predicted optical performance of the proposed path.

Abstract: A processing system including at least one processor may obtain traffic measurements for end-to-end paths in a telecommunication network, calculate traffic estimates for the end-to-end paths in future time periods based on the traffic measurements in accordance with at least one machine learning model, calculate traffic estimates for primary paths in the telecommunication network based upon the traffic estimates for the end-to-end paths, compute a backup path configuration for a primary path of the telecommunication network for the future time periods based upon the traffic estimates for the primary paths in the future time periods, detect a change in the backup path configuration for the primary path in a future time period based upon the computing, and adjust a backup path in accordance with the backup path configuration when the change in the backup path configuration is detected.

Abstract: Aspects of the disclosure include determining that a first instance of a first object is present in a first image in accordance with an execution of a first image processing algorithm, generating a first bounding region that at least partially surrounds the first instance of the first object in the first image, determining that the first instance of the first object in the first image has a first attribute in accordance with an execution of a second image processing algorithm, wherein the second image processing algorithm is operative on the first image in accordance with the first bounding region, and selecting the first instance of the first object and/or a second instance of the first object to receive a deployment of a network resource in accordance with the determining that the first instance of the first object in the first image has the first attribute. Other aspects are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, identifying a first object included in at least one image in accordance with an execution of an image processing algorithm, analyzing a plurality of parameters in accordance with at least one model responsive to the identifying of the first object included in the at least one image, wherein each parameter of the plurality of parameters is associated with the first object or a second object, selecting one of the first object or the second object for receiving at least one communication network resource responsive to the analyzing of the plurality of parameters, wherein the selecting results in a selected object, and presenting the selected object on a presentation device. Other embodiments are disclosed.

Abstract: Aspects of the disclosure include determining that a first instance of a first object is present in a first image in accordance with an execution of a first image processing algorithm, generating a first bounding region that at least partially surrounds the first instance of the first object in the first image, determining that the first instance of the first object in the first image has a first attribute in accordance with an execution of a second image processing algorithm, wherein the second image processing algorithm is operative on the first image in accordance with the first bounding region, and selecting the first instance of the first object and/or a second instance of the first object to receive a deployment of a network resource in accordance with the determining that the first instance of the first object in the first image has the first attribute. Other aspects are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, identifying a first object included in at least one image in accordance with an execution of an image processing algorithm, analyzing a plurality of parameters in accordance with at least one model responsive to the identifying of the first object included in the at least one image, wherein each parameter of the plurality of parameters is associated with the first object or a second object, selecting one of the first object or the second object for receiving at least one communication network resource responsive to the analyzing of the plurality of parameters, wherein the selecting results in a selected object, and presenting the selected object on a presentation device. Other embodiments are disclosed.

Abstract: Devices, computer-readable media and methods are disclosed for selecting paths in reconfigurable optical add/drop multiplexer (ROADM) networks using machine learning. In one example, a method includes defining a feature set for a proposed path through a wavelength division multiplexing network, wherein the proposed path traverses at least one link in the network, and wherein the at least one link connects a pair of reconfigurable optical add/drop multiplexers, predicting an optical performance of the proposed path, wherein the predicting employs a machine learning model that takes the feature set as an input and outputs a metric that quantifies predicted optical performance, and determining whether to deploy a new wavelength on the proposed path based on the predicted optical performance of the proposed path.

Abstract: Concepts and technologies are disclosed herein for creating and using network coverage models. A request for a predicted coverage model that represents a first signal propagation in a first portion of a network that covers a first area associated with a first geographic location can be received. An aerial image that depicts the first area can be obtained. The aerial image can be provided to an existing coverage model. The existing coverage model can include a neural network, and the existing coverage model can be based on a second signal propagation in a second portion of the network that covers a second area associated with a second location. The predicted coverage model for the first area can be obtained from the existing coverage model.

Abstract: A device includes a processor and a memory. The processor effectuates operations including generating a plurality of tiles for a designated region and classifying each of the plurality of tiles based on whether each tile is associated with a deployment zone. The operations further including clustering locations associated with one or more tiles of the plurality of tiles, wherein the one or more tiles are classified as being associated with the deployment zone, wherein the clustering generates at least one vertex. The operations further including forming a polygon based on the at least one vertex. The operations further including providing a map of the designated region including the polygon at a location in the map associated with the deployment zone, wherein the deployment zone is one or more areas of service within the designated region that are prioritized for new or additional services, or infrastructure based on design criteria.

Abstract: Devices, computer-readable media and methods are disclosed for selecting paths in reconfigurable optical add/drop multiplexer (ROADM) networks using machine learning. In one example, a method includes defining a feature set for a proposed path through a wavelength division multiplexing network, wherein the proposed path traverses at least one link in the network, and wherein the at least one link connects a pair of reconfigurable optical add/drop multiplexers, predicting an optical performance of the proposed path, wherein the predicting employs a machine learning model that takes the feature set as an input and outputs a metric that quantifies predicted optical performance, and determining whether to deploy a new wavelength on the proposed path based on the predicted optical performance of the proposed path.

Abstract: Concepts and technologies are disclosed herein for creating and using network coverage models. A request for a predicted coverage model that represents a first signal propagation in a first portion of a network that covers a first area associated with a first geographic location can be received. An aerial image that depicts the first area can be obtained. The aerial image can be provided to an existing coverage model. The existing coverage model can include a neural network, and the existing coverage model can be based on a second signal propagation in a second portion of the network that covers a second area associated with a second location. The predicted coverage model for the first area can be obtained from the existing coverage model.

Abstract: A processing system including at least one processor may obtain traffic measurements for end-to-end paths in a telecommunication network, calculate traffic estimates for the end-to-end paths in future time periods based on the traffic measurements in accordance with at least one machine learning model, calculate traffic estimates for primary paths in the telecommunication network based upon the traffic estimates for the end-to-end paths, compute a backup path configuration for a primary path of the telecommunication network for the future time periods based upon the traffic estimates for the primary paths in the future time periods, detect a change in the backup path configuration for the primary path in a future time period based upon the computing, and adjust a backup path in accordance with the backup path configuration when the change in the backup path configuration is detected.

Abstract: Aspects of the subject disclosure may include, for example, identifying a first object included in at least one image in accordance with an execution of an image processing algorithm, analyzing a plurality of parameters in accordance with at least one model responsive to the identifying of the first object included in the at least one image, wherein each parameter of the plurality of parameters is associated with the first object or a second object, selecting one of the first object or the second object for receiving at least one communication network resource responsive to the analyzing of the plurality of parameters, wherein the selecting results in a selected object, and presenting the selected object on a presentation device. Other embodiments are disclosed.

Abstract: Aspects of the disclosure include determining that a first instance of a first object is present in a first image in accordance with an execution of a first image processing algorithm, generating a first bounding region that at least partially surrounds the first instance of the first object in the first image, determining that the first instance of the first object in the first image has a first attribute in accordance with an execution of a second image processing algorithm, wherein the second image processing algorithm is operative on the first image in accordance with the first bounding region, and selecting the first instance of the first object and/or a second instance of the first object to receive a deployment of a network resource in accordance with the determining that the first instance of the first object in the first image has the first attribute. Other aspects are disclosed.

Abstract: A processing system including at least one processor may obtain traffic measurements for end-to-end paths in a telecommunication network, calculate traffic estimates for the end-to-end paths in future time periods based on the traffic measurements in accordance with at least one machine learning model, calculate traffic estimates for primary paths in the telecommunication network based upon the traffic estimates for the end-to-end paths, compute a backup path configuration for a primary path of the telecommunication network for the future time periods based upon the traffic estimates for the primary paths in the future time periods, detect a change in the backup path configuration for the primary path in a future time period based upon the computing, and adjust a backup path in accordance with the backup path configuration when the change in the backup path configuration is detected.

Abstract: Devices, computer-readable media and methods are disclosed for selecting paths in reconfigurable optical add/drop multiplexer (ROADM) networks using machine learning. In one example, a method includes defining a feature set for a proposed path through a wavelength division multiplexing network, wherein the proposed path traverses at least one link in the network, and wherein the at least one link connects a pair of reconfigurable optical add/drop multiplexers, predicting an optical performance of the proposed path, wherein the predicting employs a machine learning model that takes the feature set as an input and outputs a metric that quantifies predicted optical performance, and determining whether to deploy a new wavelength on the proposed path based on the predicted optical performance of the proposed path.

Abstract: Aspects of the disclosure include determining that a first instance of a first object is present in a first image in accordance with an execution of a first image processing algorithm, generating a first bounding region that at least partially surrounds the first instance of the first object in the first image, determining that the first instance of the first object in the first image has a first attribute in accordance with an execution of a second image processing algorithm, wherein the second image processing algorithm is operative on the first image in accordance with the first bounding region, and selecting the first instance of the first object and/or a second instance of the first object to receive a deployment of a network resource in accordance with the determining that the first instance of the first object in the first image has the first attribute. Other aspects are disclosed.

Abstract: A processing system including at least one processor may obtain traffic measurements for end-to-end paths in a telecommunication network, calculate traffic estimates for the end-to-end paths in future time periods based on the traffic measurements in accordance with at least one machine learning model, calculate traffic estimates for primary paths in the telecommunication network based upon the traffic estimates for the end-to-end paths, compute a backup path configuration for a primary path of the telecommunication network for the future time periods based upon the traffic estimates for the primary paths in the future time periods, detect a change in the backup path configuration for the primary path in a future time period based upon the computing, and adjust a backup path in accordance with the backup path configuration when the change in the backup path configuration is detected.