Abstract: In one embodiment, a method includes receiving, by a mobile edge computing (MEC) controller and from an application analytic engine, a registration request for an application. The registration request includes a request for MEC key performance indicators (KPIs). The method also includes communicating, by the MEC controller, MEC data associated with a first MEC host and a second MEC host to the application analytic engine. The method further includes receiving, by the MEC controller, MEC policies from the application analytic engine, determining to host the application in the first MEC host based on the MEC policies, and communicating the MEC policies to the first MEC host.

Abstract: Methods are provided in which devices vulnerable to performance degradation during a network offload process are identified and handled accordingly. In these methods, a network controller obtains data associated with operational performance of devices in a radio network during one or more offload procedures in which the devices are to be transitioned off of the radio network. The network controller further analyzes the data to determine impact to at least one performance parameter of the devices during the one or more offload procedures, and during a subsequent offload procedure, the network controller orchestrates offloading of the devices according to the impact determined to the at least one performance parameter based on the analyzing.

Abstract: In one embodiment, a method includes receiving, by a mobile edge computing (MEC) controller and from an application analytic engine, a registration request for an application. The registration request includes a request for MEC key performance indicators (KPIs). The method also includes communicating, by the MEC controller, MEC data associated with a first MEC host and a second MEC host to the application analytic engine. The method further includes receiving, by the MEC controller, MEC policies from the application analytic engine, determining to host the application in the first MEC host based on the MEC policies, and communicating the MEC policies to the first MEC host.

Abstract: Systems, methods, and storage media for generating and/or recoverying an end-to-end hybrid cloud snapshot are disclosed. Exemplary implementations may: receive application specific triggering criteria for generating an end-to-end hybrid cloud snapshot; trigger a snapshot event based on the application specific triggering criteria and a machine learning model; accumulate snapshot portions from a plurality of nodes in a hybrid cloud network; and store, in a snapshot database, the end-to-end hybrid cloud snapshot comprising the snapshot portions from the plurality of nodes. Exemplary implementations may further include: generate a recovery model based on a snapshot database, wherein the snapshot database comprises a plurality of network snapshots associated with a network, receive parameters for a desired recovery state, and identify a recommended network snapshot from the plurality of network snapshots based on the recovery model and the parameters for the desired recovery state.

Abstract: In one embodiment, a method includes receiving, by a mobile edge computing (MEC) controller and from an application analytic engine, a registration request for an application. The registration request includes a request for MEC key performance indicators (KPIs). The method also includes communicating, by the MEC controller, MEC data associated with a first MEC host and a second MEC host to the application analytic engine. The method further includes receiving, by the MEC controller, MEC policies from the application analytic engine, determining to host the application in the first MEC host based on the MEC policies, and communicating the MEC policies to the first MEC host.

Abstract: In one embodiment, a machine learning server in a computer network determines a plurality of computing features shared across a given set of computing products, and collects, from each computing product of the given set, problem-solution data for each computing feature of the plurality of computing features. Problem-solution data is indicative of problems related to a respective computing feature, attempted solution actions for the problems, and outcomes of the attempted solutions on the problem. The machine learning server updates a machine learning model of suggested solutions for computing-feature-specific problems based on the collected problem-solution data, and provides, based on the machine learning model, a particular suggested solution for a particular computing-feature-specific problem to a particular computing product.

Abstract: Systems and methods provide for managing a power consumption policy for a powered device. A network controller may be configured to receive power consumption information for a powered device (PD) connected to power sourcing equipment (e.g., a PSE device), calculate a predicted threshold for the PD based on the power consumption information for the PD, and transmit the predicted threshold for the PD to the PSE device. Power sourcing equipment is configured to calculate power consumption information for the PD, transmit the power consumption information for the PD to the network controller, receive a predicted threshold for the PD, and implement a power consumption policy for the PD based on the predicted threshold.

Abstract: Methods are provided in which devices vulnerable to performance degradation during a network offload process are identified and handled accordingly. In these methods, a network controller obtains data associated with operational performance of devices in a radio network during one or more offload procedures in which the devices are to be transitioned off of the radio network. The network controller further analyzes the data to determine impact to at least one performance parameter of the devices during the one or more offload procedures, and during a subsequent offload procedure, the network controller orchestrates offloading of the devices according to the impact determined to the at least one performance parameter based on the analyzing.

Abstract: In one embodiment, a method includes receiving, by a mobile edge computing (MEC) controller and from an application analytic engine, a registration request for an application. The registration request includes a request for MEC key performance indicators (KPIs). The method also includes communicating, by the MEC controller, MEC data associated with a first MEC host and a second MEC host to the application analytic engine. The method further includes receiving, by the MEC controller, MEC policies from the application analytic engine, determining to host the application in the first MEC host based on the MEC policies, and communicating the MEC policies to the first MEC host.

Abstract: Methods are provided in which devices vulnerable to performance degradation during a network offload process are identified and handled accordingly. In these methods, a network controller obtains data associated with operational performance of devices in a radio network during one or more offload procedures in which the devices are to be transitioned off of the radio network. The network controller further analyzes the data to determine impact to at least one performance parameter of the devices during the one or more offload procedures, and during a subsequent offload procedure, the network controller orchestrates offloading of the devices according to the impact determined to the at least one performance parameter based on the analyzing.

Abstract: Systems, methods, and computer-readable media are disclosed for implementing a load balance update. A system may be configured to receive a plurality performance reports for a plurality of service nodes running on the plurality of host entities, wherein each performance report comprises performance indicators for a service node in the plurality of service nodes running on a host entity in the plurality of host entities, generate a load balance update based on the performance reports, and implement the load balance update at a load balancer.

Abstract: Methods are provided in which devices vulnerable to performance degradation during a network offload process are identified and handled accordingly. In these methods, a network controller obtains data associated with operational performance of devices in a radio network during one or more offload procedures in which the devices are to be transitioned off of the radio network. The network controller further analyzes the data to determine impact to at least one performance parameter of the devices during the one or more offload procedures, and during a subsequent offload procedure, the network controller orchestrates offloading of the devices according to the impact determined to the at least one performance parameter based on the analyzing.

Abstract: Systems and methods provide for managing a power consumption policy for a powered device. A network controller may be configured to receive power consumption information for a powered device (PD) connected to power sourcing equipment (e.g., a PSE device), calculate a predicted threshold for the PD based on the power consumption information for the PD, and transmit the predicted threshold for the PD to the PSE device. Power sourcing equipment is configured to calculate power consumption information for the PD, transmit the power consumption information for the PD to the network controller, receive a predicted threshold for the PD, and implement a power consumption policy for the PD based on the predicted threshold.

Abstract: An antenna may be provided. First, a Machine Learning (ML) model may be used, at a predetermined time, to predict a probable location of at least one of a plurality of client devices. Next, spatial coordinates may be obtained for the probable location from the ML model. Then an antenna sub-array of an antenna array may be steered toward the spatial coordinates of the probable location. The antenna sub-array may be unallocated.

Abstract: Systems, methods, and storage media for generating and/or recoverying an end-to-end hybrid cloud snapshot are disclosed. Exemplary implementations may: receive application specific triggering criteria for generating an end-to-end hybrid cloud snapshot; trigger a snapshot event based on the application specific triggering criteria and a machine learning model; accumulate snapshot portions from a plurality of nodes in a hybrid cloud network; and store, in a snapshot database, the end-to-end hybrid cloud snapshot comprising the snapshot portions from the plurality of nodes. Exemplary implementations may further include: generate a recovery model based on a snapshot database, wherein the snapshot database comprises a plurality of network snapshots associated with a network, receive parameters for a desired recovery state, and identify a recommended network snapshot from the plurality of network snapshots based on the recovery model and the parameters for the desired recovery state.

Abstract: Systems, methods, and computer-readable media are disclosed for implementing a load balance update. A system may be configured to receive a plurality performance reports for a plurality of service nodes running on the plurality of host entities, wherein each performance report comprises performance indicators for a service node in the plurality of service nodes running on a host entity in the plurality of host entities, generate a load balance update based on the performance reports, and implement the load balance update at a load balancer.

Abstract: In one example, a collaboration application presents a map of a geographic area that includes a network connected device. The map includes a graphical representation of the network connected device. The graphical representation has a location on the map that corresponds to a current physical location of the network connected device in the geographic area. The collaboration application receives an indication of a user selection of the graphical representation, and, in response to receiving the indication, determines that the user selection of the graphical representation corresponds to a user selection of the network connected device. The collaboration application sends, to the network connected device, an invitation to join a collaboration meeting as a peer member of the collaboration meeting.

Abstract: In one embodiment, a machine learning server in a computer network determines a plurality of computing features shared across a given set of computing products, and collects, from each computing product of the given set, problem-solution data for each computing feature of the plurality of computing features. Problem-solution data is indicative of problems related to a respective computing feature, attempted solution actions for the problems, and outcomes of the attempted solutions on the problem. The machine learning server updates a machine learning model of suggested solutions for computing-feature-specific problems based on the collected problem-solution data, and provides, based on the machine learning model, a particular suggested solution for a particular computing-feature-specific problem to a particular computing product.

Abstract: A method for managing power consumption in a stackable switch system, the method comprises determining a power management strategy for a plurality of switches in a stackable switch system. The method also comprises determining, by a processor associated with a first switch of the stackable switch system, that at least one criterion for causing the second switch to enter a power save mode has been met, based on the determined power management strategy. The method further comprises providing a first command for causing a second switch of the stackable switch system to enter the power save mode. The method also comprises determining that at least one criterion for causing the second switch to exit the power save mode has been met, based on the determined power management strategy. The method further comprises providing a second command for causing the second switch to wake from the power save mode.