Predictive model-based intelligent system for automatically scaling and managing provisioned computing resources

A method for automatically scaling provisioned resources, comprising:

• receiving an indication to execute a set of tasks;
• determining a required number of processing threads for the set of tasks, comprising to:
• randomly ordering the set of tasks to obtain an ordered set of tasks;
• allocating a first task of the ordered set of tasks to a first processing thread of a set of processing threads;
• determining whether at least one processing thread of the set of processing threads has an available capacity for a next processing task of the ordered set of processing tasks;
• in response to determining that no processing thread of the set of processing threads has the available capacity for the next processing task, creating a new processing thread of the set of processing threads and allocating the next processing task to the new processing thread;
• and
• in response to determining that there are no more tasks of the ordered set of tasks to allocate to the set of processing threads, indicating the required number of processing threads for the set of tasks is a count of the processing threads in the set of processing threads;
• determining, using a processor, required resources for processing the required number of processing threads using a model;
• provisioning the required resources;
• indicating to execute client tasks using the provisioned resources;
• determining server telemetry or logging data for the provisioned resources;
• providing the server telemetry or the logging data to the model;
• determining a resource utilization score based at least in part on the server telemetry or the logging data;
• determining a provisioning performance reward based at least in part on the resource utilization score;
• and
• adjusting model parameters using the provisioning performance reward.

A computer program product for automatically scaling provisioned resources, the computer program product being embodied in a non-transitory computer readable storage medium and comprising computer instructions for:

• receiving an indication to execute a set of tasks;
• determining a required number of processing threads for the set of tasks, comprising to:
• randomly ordering the set of tasks to obtain an ordered set of tasks;
• allocating a first task of the ordered set of tasks to a first processing thread of a set of processing threads;
• determining whether at least one processing thread of the set of processing threads has an available capacity for a next processing task of the ordered set of processing tasks;
• in response to determining that no processing thread of the set of processing threads has the available capacity for the next processing task, creating a new processing thread of the set of processing threads and allocating the next processing task to the new processing thread;
• and
• in response to determining that there are no more tasks of the ordered set of tasks to allocate to the set of processing threads, indicating the required number of processing threads for the set of tasks is a count of the processing threads in the set of processing threads;
• determining required resources for processing the required number of processing threads using a model;
• provisioning the required resources;
• indicating to execute client tasks using the provisioned resources;
• determining server telemetry or logging data for the provisioned resources;
• providing the server telemetry or the logging data to the model;
• determining a resource utilization score based at least in part on the server telemetry or the logging data;
• determining a provisioning performance reward based at least in part on the resource utilization score;
• and
• adjusting model parameters using the provisioning performance reward.