	US 12,130,811 B2
	Task-execution planning using machine learning
Qiming Jiang, Redmond, WA (US); Orestis Kostakis, Redmond, WA (US); and John Reumann, Kirkland, WA (US)
Assigned to Snowflake Inc., Bozeman, MT (US)
Filed by Snowflake Inc., Bozeman, MT (US)
Filed on Jul. 31, 2023, as Appl. No. 18/362,869.
Application 18/362,869 is a continuation of application No. 18/104,256, filed on Jan. 31, 2023, granted, now 11,755,576.
Application 18/104,256 is a continuation of application No. 17/930,277, filed on Sep. 7, 2022, granted, now 11,620,289.
Prior Publication US 2024/0078235 A1, Mar. 7, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. G06F 16/00 (2019.01); G06F 16/2453 (2019.01); G06F 16/27 (2019.01)

CPC G06F 16/24542 (2019.01) [G06F 16/27 (2019.01)]

14 Claims

1. A method comprising:

receiving a task to be executed using resources of a computing cluster;

generating a task execution plan;

accessing information about data to be used for the task;

predicting, by at least one hardware processor, resource requirements for executing the task by applying machine learning to the task execution plan and the information about the data;

determining a total amount of time from submission of the task to termination of the task, the determining the total amount of the time comprising:

generating a prediction profile; and

managing assignment of a query based on resource data for the task; and

generating assignment data to execute the task on the resources by applying machine learning information about a current state of the resources and the predicted resource requirements.