Application prioritization system

A method comprising:

• storing computing task rules associated with a computing infrastructure, wherein:
• the computing infrastructure comprising a plurality of computing devices configured to implement computing applications, wherein execution of computing tasks associated with the computing applications is distributed amongst the plurality of computing devices;
• and
• the computing task rules comprise, for each type of the computing tasks associated with the computing applications, a predefined value indicating an extent to which the type of the computing task is critical for meeting a predefined computing infrastructure demand;
• and
• receiving application data associated with the computing applications, wherein the application data comprises, for each computing application, characteristics of the computing application and users of the computing application;
• receiving a first user request to determine a priority of a first computing application of the computing applications compared to a second computing application of the computing applications;
• determining, by applying a feedback-based machine learning model to at least a portion of the application data, the first user request, and the computing task rules, a first priority of the first computing application and a second priority of the second computing application and an explanation of the first and second priorities, wherein the first and second priorities provide an indication of whether the first computing application or the second computing application is more critical to functions of the computing infrastructure, wherein determining comprises:
• iteratively determining, using a combination of linear regression and machine learning, factors and corresponding weights for the first computing application and the second computing application, wherein each of the factors corresponds to a machine-learning-defined portion of the application data;
• determining the first priority for the first computing application based on the factors and corresponding weights for the first computing application;
• determining the second priority for the second computing application based on the factors and corresponding weights for the second computing application;
• and
• determining a larger of the first priority and the second priority;
• providing a response comprising an indication of the larger of the first priority and second priority;
• and
• executing one or more of the computing tasks associated with one or more of the computing applications based on the adjusted allocation.

A system comprising:

• a memory operable to store computing task rules associated with a computing infrastructure, wherein:
• the computing infrastructure comprises a plurality of computing devices configured to implement computing applications, wherein execution of computing tasks associated with the computing applications is distributed amongst the plurality of computing devices;
• and
• the computing task rules comprise, for each type of the computing tasks associated with the computing applications, a predefined value indicating an extent to which the type of the computing task is critical for meeting a predefined computing infrastructure demand;
• and
• a processor communicatively coupled to the memory and configured to:
• receive application data associated with the computing applications, wherein the application data comprises, for each computing application, characteristics of the computing application and users of the computing application;
• receive a first user request to determine a priority of a first computing application of the computing applications compared to a second computing application of the computing applications;
• determine, by applying a feedback-based machine learning model to at least a portion of the application data, the first user request, and the computing task rules, a first priority of the first computing application and a second priority of the second computing application and an explanation of the first and second priorities, wherein the first and second priorities provide an indication of whether the first computing application or the second computing application is more critical to functions of the computing infrastructure, wherein determining comprises:
• iteratively determining, using a combination of linear regression and machine learning, factors and corresponding weights for the first computing application and the second computing application, wherein each of the factors corresponds to a machine-learning-defined portion of the application data;
• determining the first priority for the first computing application based on the factors and corresponding weights for the first computing application;
• determining the second priority for the second computing application based on the factors and corresponding weights for the second computing application;
• provide a response comprising an indication of the larger of the first priority and second priority;
• and
• execute one or more of the computing tasks associated with one or more of the computing applications based on the adjusted allocation.