In one aspect, the present disclosure relates to a method comprising: receiving, at a client device, information from a node manager about a plurality of nodes in a computer cluster, the information comprising a network address associated each of the plurality of nodes and sending, by the client device, a request to a load balancer to access a first node from the plurality of nodes, the request comprising a first URL including an encoded representation of the network address associated with the first node. The load balancer is configured to determine the request should be routed to a first network address based on decoding the URL, the first network address associated with a first node from the plurality of nodes and forward the request to the first node in response to the determining.

In one aspect, the present disclosure relates to a method comprising: receiving, at a client device, information from a node manager about a plurality of nodes in a computer cluster, the information comprising a network address associated each of the plurality of nodes and sending, by the client device, a request to a load balancer to access a first node from the plurality of nodes, the request comprising a first URL including an encoded representation of the network address associated with the first node. The load balancer is configured to determine the request should be routed to a first network address based on decoding the URL, the first network address associated with a first node from the plurality of nodes and forward the request to the first node in response to the determining.

A method includes receiving a request from a vehicle to perform a computing task, selecting a machine learning model from among a plurality of machine learning models based at least in part on the request, and predicting an amount of computing resources needed to perform the computing task using the selected machine learning model.

A method includes receiving a request from a vehicle to perform a computing task, selecting a machine learning model from among a plurality of machine learning models based at least in part on the request, and predicting an amount of computing resources needed to perform the computing task using the selected machine learning model.

Mobile device security, device management, and policy enforcement are described in a cloud-based system where the “cloud” is used to pervasively enforce security and policy and perform device management regardless of device type, platform, location, etc. A method includes receiving one or more mobile profiles for one or more mobile devices each associated with a user from an enterprise; responsive to enrollment of a mobile device of the one or more mobile devices, communicating to the mobile device; determining an associated mobile profile of the one or more mobile profiles for the mobile device; and configuring the mobile device based on the associated mobile profile.

Mobile device security, device management, and policy enforcement are described in a cloud-based system where the “cloud” is used to pervasively enforce security and policy and perform device management regardless of device type, platform, location, etc. A method includes receiving one or more mobile profiles for one or more mobile devices each associated with a user from an enterprise; responsive to enrollment of a mobile device of the one or more mobile devices, communicating to the mobile device; determining an associated mobile profile of the one or more mobile profiles for the mobile device; and configuring the mobile device based on the associated mobile profile.

A method and apparatus for controlling power production. In one embodiment, the method comprises determining a predicted weather event; determining a predicted power production impact for a distributed generator (DG) array based on the predicted weather event; and controlling power production from one or more components of the DG array to compensate for the predicted power production impact.

A method and apparatus for controlling power production. In one embodiment, the method comprises determining a predicted weather event; determining a predicted power production impact for a distributed generator (DG) array based on the predicted weather event; and controlling power production from one or more components of the DG array to compensate for the predicted power production impact.

Techniques are disclosed for generating a virtual representation (e.g., one or more digital twin models) of a multi-access edge computing system environment, and managing the multi-access edge computing system environment via the virtual representation. By way of example only, such techniques enable understanding, prediction and/or optimization of performance of applications and/or systems operating in the multi-access edge computing environment.

Disclosed herein are system, method, and computer program product embodiments for providing an API description of an external network service and using the API to integrate the external service into a network. An embodiment operates by receiving, from a service provider, a description of an application programming interface (API), transmitting a call to the service provider using the API for creating a new instance of a service and transmitting to the service provider a traffic flow upon which the service will be applied.