Runtime environment determination for software containers

A system comprising:

• a processor;
• and
• a memory in communication with the processor, the memory containing instructions that, when executed by the processor, cause the processor to:
• identify static parameters of a software container, wherein the static parameters relate to metadata of the software container itself;
• assign the software container to a selected runtime environment based on the static parameters using a first machine learning model;
• identify runtime parameters for the software container by analyzing the software container at runtime, where the runtime parameters relate to operations required during runtime by the software container;
• using a second machine learning model, in response to a determination that the selected runtime environment matches the runtime parameters, continuing to running the software container in the selected runtime environment;
• using the second machine learning model, in response to a second determination that the selected runtime environment does not match the runtime parameter, running the software container in a different runtime environment that matches both the static and runtime parameters;
• gathering, on a predetermined schedule, characteristics of the software container with correlations that dictate performance changes in the different runtime environment;
• wherein the characteristics include stability of the runtime environments and latency for each runtime environment introduced by a computer processing contention;
• generating training data based on the correlations between the characteristics and the performance changes in the different runtime environment;
• and
• retraining the second machine learning model based on the training data.

A computer program product, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to:

• identify static parameters of a runtime environment for a software container, wherein the static parameters relate to metadata of the software container itself;
• assign the software container to a selected runtime environment based on the static parameters using a first machine learning model;
• identify runtime parameters for the software container by analyzing the software container at runtime, where the runtime parameters relate to operations required during runtime by the software container;
• using a second machine learning model, in response to a determination that the selected runtime environment matches the runtime parameters, continuing to run the software container in the selected runtime environment;
• using the second machine learning model, in response to a second determination that the selected runtime environment does not match the runtime parameter, running the software container in a different runtime environment that matches both the static and runtime parameters;
• gathering, on a predetermined schedule, characteristics of the software container with correlations that dictate performance changes in the different runtime environment;
• wherein the characteristics include stability of the different runtime environments and latency for each runtime environment introduced by a computer processing contention;
• generating training data based on the correlations between the characteristics and the performance changes in the different runtime environment;
• and
• retraining the second machine learning model based on the training data.