Abstract: A computer-implemented method classifies and creates a container file based on non-functional parameters. The method includes analyzing, by a learning model, a codebase. The codebase includes code for one or more applications. The method also includes identifying, based on the analyzing, a set of functional requirements for each application and a set of non-functional parameters. The method further includes classifying a first application of the one or more applications with a first non-functional parameter. The method includes generating a first container file for the first application. The first container file includes the functional requirements for the first application and the first non-functional parameter. The method further includes creating a first container from the first container file.

Abstract: Technology for implementing methods that include the following operations: (i) receiving a container instantiation data set that includes the data needed to instantiate at least a first type of container; (ii) receiving a computing environment context data set including information relevant to computing operations currently being performed in a computer environment, with the computer environment context data set including information indicative of a first internal factor and/or a first external factor; (iii) applying an artificial intelligence algorithm to determine a predicted future status of the computing environment based, at least in part, upon the computer environment context data set; (iv) determining, by machine logic and based, at least in part upon the predicted future status, that the computing environment is likely to operate better if a new container of the first type is instantiated; and (v) automatically instantiating a first container.

Abstract: Epidemiological modeling and simulation includes generating a virtual representation of a predetermined geospatial region by communicating in real time via a data communications network with a plurality of sensor-endowed computing nodes that capture and convey sensor-generated data in real time to the networked computer. A causal network is derived for mapping biodata onto inferences regarding pathogenic vector dynamics of a known pathogen based on stochastic vector probabilities. The biodata can be culled from historical data associated with the predetermined geospatial region. A visualization of selected characteristics of the predetermined geospatial region is generated and a corresponding epidemiological model created based on the historical data. Sensor-generated data and historical data are correlated. An expected effect of the known pathogen on a population of the geospatial region is predicted using a model simulation based on correlating the sensor-generated data and historical data.

Abstract: A computer-implemented method classifies and creates a container file based on non-functional parameters. The method includes analyzing, by a learning model, a codebase. The codebase includes code for one or more applications. The method also includes identifying, based on the analyzing, a set of functional requirements for each application and a set of non-functional parameters. The method further includes classifying a first application of the one or more applications with a first non-functional parameter. The method includes generating a first container file for the first application. The first container file includes the functional requirements for the first application and the first non-functional parameter. The method further includes creating a first container from the first container file.