Auto scaling a distributed predictive analytics system with machine learning

A system for creating an auto-scaled predictive analytics model comprising:

• a processor configured to:
• determine whether a queue size of a service master queue is greater than zero;
• responsive to determining that the queue size is greater than zero, fetch a count of requests in a plurality of requests in the service master queue, and a type for each of the requests;
• derive a value for time required for each of the requests;
• retrieve a number of available processing nodes based on the time required for each of the requests;
• auto-scale a processing node number responsive to determining that a total execution time for all of the requests in the plurality of requests exceeds a predetermined time value;
• and
• output an auto-scaled predictive analytics model based on the processing node number and queue size.

A computer program product for creating an auto-scaled predictive analytics model, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to perform a method comprising:

• determining, via a processor, whether a queue size of a service master queue is greater than zero;
• responsive to determining that the queue size is greater than zero, fetching, via the processor, a count of requests in a plurality of requests in the service master queue, and a type for each of the requests;
• deriving via the processor, a value for time required for each of the requests;
• retrieving, via the processor, a number of available processing nodes based on the time required for each of the requests;
• auto-scaling, via the processor, a processing node number responsive to determining that a total execution time for all of the requests in the plurality of requests exceeds a predetermined time value;
• and
• outputting, via the processor, an auto-scaled predictive analytics model based on the processing node number and queue size.