System for computational resource prediction and subsequent workload provisioning

A method for scheduling user tasks on a computer cluster including a set of computing platforms each corresponding to one of a plurality of different computing architectures, the method comprising:

• receiving input data, the input data comprising platform-independent task parameters and target metrics associated with a user task;
• automatically generating a computing architecture and computing platform selection and computing resource prediction based on the input data and a hardware profile of the computer cluster using a computing resource prediction engine;
• automatically mapping the user task to one or more of the set of computing platforms and to a scheduling action among a set of scheduling actions using a trained intelligent computing resource assignment engine;
• automatically scheduling the user task according to the scheduling action;
• and
• automatically adjusting the intelligent computing resource assignment engine by applying reinforcement learning based on newly acquired data associated with performing the scheduling action.

A non-transitory medium for storing computer readable instructions, the computer readable instructions, when executed by a processor, are configured to cause the processor to schedule user tasks on a computer cluster including a set of computing platforms each corresponding to one of a plurality of different computing architectures by:

• receiving input data, the input data comprising platform-independent task parameters and target metrics associated with a user task;
• automatically generating a computing architecture and computing platform selection and computing resource prediction based on the input data and a hardware profile of the computer cluster using a computing resource prediction engine;
• automatically mapping the user task to one or more of the set of computing platforms and to a scheduling action among a set of scheduling actions using a trained intelligent computing resource assignment engine;
• automatically scheduling the user task according to the scheduling action;
• and
• automatically adjusting the intelligent computing resource assignment engine by applying reinforcement learning based on newly acquired data associated with performing the scheduling action.