Distributed artifical intelligence workload optimizer

A method, comprising:

• at a computing platform comprising at least one processor, a communication interface, and memory:
• receiving, by the at least one processor, a workload, wherein the workload identifies a number of computer processing cycles required to complete a task;
• receiving, by the at least one processor, from a computing device associated with a user, one or more processing constraints;
• receiving, by the at least one processor, contextual parameters associated with the workload, wherein the contextual parameters include geographic information, weather related information, historical information, temporal information, and stock market information;
• acquiring, by the at least one processor, availability data for a plurality of resources in a distributed computing environment, wherein each of the plurality of resources is capable of performing at least part of the workload;
• based on the one or more processing constraints, the contextual parameters, and the availability data, building, by the at least one processor, using an artificial intelligence algorithm, an optimization model for distributing the workload, wherein the optimization model optimizes distribution of available resources of the plurality of resources allocated to executing the workload;
• based on the optimization model, identifying, by the at least one processor, one or more resource distribution options including an optimal distribution of the available resources for executing the workload;
• and
• executing, by the at least one processor, the workload based on the identified one or more resource distribution options;
• detecting, by the at least one processor, one or more newly-added resources in the distributed computing environment during execution of the workload;
• acquiring, by the at least one processor, updated availability data for the one or more newly-added resources;
• updating, by the at least one processor, the optimization model based on the updated availability data;
• based on the updated optimization model, modifying, by the at least one processor, the one or more resource distribution options;
• prompting, by the at least one processor, a user of the computing device to select one of the modified one or more resource distribution options;
• and
• adjusting, by the at least one processor, execution of the workload based on the selection by the user of the computing device.

One or more non-transitory computer-readable media storing instructions that, when executed by a computing platform comprising at least one processor, memory, and a communication interface, cause the computing platform to:

• receive a workload, wherein the workload identifies a number of computer processing cycles required to complete a task;
• receive, from a computing device associated with a user, one or more processing constraints;
• receive contextual parameters associated with the workload, wherein the contextual parameters include geographic information, weather related information, historical information, temporal information, and stock market information;
• acquire availability data for a plurality of resources in a distributed computing environment, wherein each of the plurality of resources is capable of performing at least part of the workload;
• based on the one or more processing constraints, the contextual parameters, and the availability data, build, using an artificial intelligence algorithm, an optimization model for distributing the workload, wherein the optimization model optimizes distribution of available resources of the plurality of resources allocated to executing the workload;
• based on the optimization model, identify one or more resource distribution options including an optimal distribution of the available resources for executing the workload;
• and
• execute the workload based on the identified one or more resource distribution options;
• detect one or more newly-added resources in the distributed computing environment during execution of the workload;
• acquire updated availability data for the one or more newly-added resources;
• update the optimization model based on the updated availability data;
• based on the updated optimization model, modify the one or more resource distribution options;
• prompt a user of the computing device to select one of the modified one or more resource distribution options;
• and
• adjust execution of the workload based on the selection by the user of the computing device.