System and method for data driven scaling of computing resource allocation via machine learning

A computer program product for data driven scaling of computing resource allocation via machine learning, the computer program product comprising a non-transitory computer-readable medium comprising code causing an apparatus to:

• receive interaction data from each endpoint device within an endpoint device group, the interaction data comprising utilizing of an application, and metadata of at least one of a user identifier, time, location, role, weather, holidays, and current events, wherein the application utilizes an allocated portion of computing resources located at a server cluster, the allocated portion being specific to the application and scalable;
• log, in usage logs, the interaction data;
• determine, from the usage logs, a usage pattern associated with the application, the usage pattern comprising extracted trends and an impact on the utilization of the application based on the extracted trends;
• predict, using the usage pattern, a predicted application usage for a predetermined time;
• compile the predicted application usage into a compiled application usage;
• and
• scale, at the predetermined time and based on the compiled application usage, the allocated portion of the computing resources.

A method for data driven scaling of computing resource allocation via machine learning, the method comprising:

• receiving interaction data from each endpoint device within an endpoint device group, the interaction data comprising utilizing of an application, and metadata of at least one of a user identifier, time, location, role, weather, holidays, and current events, wherein the application utilizes an allocated portion of computing resources located at a server cluster, the allocated portion being specific to the application and scalable;
• logging, in usage logs, the interaction data;
• determining, from the usage logs, a usage pattern associated with the application, the usage pattern comprising extracted trends and an impact on the utilization of the application based on the extracted trends;
• predicting, using the usage pattern, a predicted application usage for a predetermined time;
• compiling the predicted application usage into a compiled application usage;
• and
• scaling, at the predetermined time and based on the compiled application usage, the allocated portion of the computing resources.