Abstract: A method includes collecting, at a monitoring and recovery node, a virtual network function key performance index data through multiple channels from a corresponding containerized virtual network function. The method includes maintaining, at the monitoring and recovery node, state information of the corresponding containerized virtual network function and running, at the monitoring and recovery node, a machine learning algorithm that, once trained, learns and predicts whether the corresponding containerized virtual network function requires one of a scaling, a healing or a context switching to sister virtual network function to yield a determination and meet the service level agreement of a network service.

Abstract: A method includes collecting, at a monitoring and recovery node, a virtual network function key performance index data through multiple channels from a corresponding containerized virtual network function. The method includes maintaining, at the monitoring and recovery node, state information of the corresponding containerized virtual network function and running, at the monitoring and recovery node, a machine learning algorithm that, once trained, learns and predicts whether the corresponding containerized virtual network function requires one of a scaling, a healing or a context switching to sister virtual network function to yield a determination and meet the service level agreement of a network service.

Abstract: A computer-implemented method is disclosed for generating Dojo-conforming JavaScript Object Notation (JSON) objects for base objects of an object-oriented programming environment that have been annotated to denote which attribute declarations of the base object correspond to identified Dojo attributes. In a Java class, for example, annotations may be made to indicate which class attributes correspond to the identifier attribute of a Dojo JSON object and based on those annotations a Dojo-conforming JSON object may be generated.

Abstract: A computer-implemented method is disclosed for generating Dojo-conforming JavaScript Object Notation (JSON) objects for base objects of an object-oriented programming environment that have been annotated to denote which attribute declarations of the base object correspond to identified Dojo attributes. In a Java class, for example, annotations may be made to indicate which class attributes correspond to the identifier attribute of a Dojo JSON object and based on those annotations a Dojo-conforming JSON object may be generated.