Dynamic scaling of virtual containers in a containerized environment

A method, comprising:

• obtaining a machine learning algorithm configured, when executed, to analyze and structure information about resources assigned in a containerized environment;
• obtaining historical data representative of one or more network resources assigned in the containerized environment over a first time period;
• determining first network resource availability information in a communication network, the first network resource availability information indicating first network resources available for assignment in the containerized environment over a second time period;
• execute the machine learning algorithm to perform one or more operations comprising:
• analyzing the first network resource availability information based at least in part upon a first plurality of communication conditions;
• in response to analyzing the first network resource availability information, generating a first plurality of analysis results;
• and
• generating a first plurality of network assignment recommendations based at least in part upon the first plurality of analysis results and the historical data;
• assigning a second plurality of resources in the containerized environment over the second time period;
• and
• rescaling one or more virtual containers in the containerized environment to use the second plurality of resources.

A non-transitory computer-readable medium storing instructions that when executed by a processor cause the processor to:

• obtain a machine learning algorithm configured, when executed, to analyze and structure information about resources assigned in a containerized environment;
• obtain historical data representative of one or more network resources assigned in the containerized environment over a first time period;
• determine first network resource availability information in a communication network, the first network resource availability information indicating first network resources available for assignment in the containerized environment over a second time period;
• execute the machine learning algorithm to:
• analyze the first network resource availability information based at least in part upon a first plurality of communication conditions;
• in response to analyzing the first network resource availability information, generate a first plurality of analysis results;
• and
• generate a first plurality of network assignment recommendations based at least in part upon the first plurality of analysis results and the historical data;
• assign a second plurality of resources in the containerized environment over the second time period;
• and
• rescale one or more virtual containers in the containerized environment to use the second plurality of resources.